# Federated Learning in Healthcare: Model Misconducts, Security, Challenges,
Applications, and Future Research Directions-A Systematic Review

Md Shahin Ali
Department of Biomedical Engineering
Islamic University
Kushtia 7003, Bangladesh
<EMAIL_ADDRESS>
&Md Manjurul Ahsan
Department of Industrial and Systems Engineering
University of Oklahoma
Norman, Oklahoma-73071
<EMAIL_ADDRESS>
&Lamia Tasnim
Department of Food and Nutrition
Government College of Applied Human Science
Azimpur, Dhaka 1205, Bangladesh
<EMAIL_ADDRESS>
&Sadia Afrin
Department of Food and Nutrition
Government College of Applied Human Science
Azimpur, Dhaka 1205, Bangladesh
<EMAIL_ADDRESS>
&Koushik Biswas
Department of Computer Science and Engineering
IIIT Delhi, New Delhi
Delhi 110020, India
<EMAIL_ADDRESS>
&Md Maruf Hossain
Department of Biomedical Engineering
Islamic University
Kushtia 7003, Bangladesh
<EMAIL_ADDRESS>
&Md Mahfuz Ahmed
Department of Biomedical Engineering
Islamic University
Kushtia 7003, Bangladesh
<EMAIL_ADDRESS>
&Ronok Hashan
Department of Electrical and Electronic Engineering
Islamic University
Kushtia 7003, Bangladesh
<EMAIL_ADDRESS>
&Md Khairul Islam
Department of Biomedical Engineering
Islamic University
Kushtia 7003, Bangladesh
<EMAIL_ADDRESS>
&Shivakumar Raman
Department of Industrial and Systems Engineering
University of Oklahoma
Norman, Oklahoma-73071
<EMAIL_ADDRESS>
###### Abstract

Data privacy has become a major concern in healthcare due to the increasing
digitization of medical records and data-driven medical research. Protecting
sensitive patient information from breaches and unauthorized access is
critical, as such incidents can have severe legal and ethical complications.
Federated Learning (FL) addresses this concern by enabling multiple healthcare
institutions to collaboratively learn from decentralized data without sharing
it. FL’s scope in healthcare covers areas such as disease prediction,
treatment customization, and clinical trial research. However, implementing FL
poses challenges, including model convergence in non-IID (independent and
identically distributed) data environments, communication overhead, and
managing multi-institutional collaborations. A systematic review of FL in
healthcare is necessary to evaluate how effectively FL can provide privacy
while maintaining the integrity and usability of medical data analysis. In
this study, we analyze existing literature on FL applications in healthcare.
We explore the current state of model security practices, identify prevalent
challenges, and discuss practical applications and their implications.
Additionally, the review highlights promising future research directions to
refine FL implementations, enhance data security protocols, and expand FL’s
use to broader healthcare applications, which will benefit future researchers
and practitioners.

_K_ eywords Federated Learning $\cdot$ Healthcare $\cdot$ Data Privacy $\cdot$
Machine Learning $\cdot$ Security

##  1 Introduction

What is Federated Learning? Federated Learning (FL) is a machine learning (ML)
framework that enables clients spread across different geographical locations
to collectively train a shared model without directly exchanging local data
[1]. This framework tackles challenges like class imbalances and non-IID (non-
independent and identically distributed) data distributions across clients.
These issues can cause the “client-drift" problem, resulting in slower
convergence and suboptimal model performance [2]. By periodically
redistributing model updates and employing methods like adaptive self-
distillation and data harmonization, FL improves model training while
maintaining data privacy and security in distributed environments.

What are the application domains of FL? FL is becoming increasingly popular
due to its effectiveness in training models while maintaining data security.
There are several application domains where FL is making significant
contributions. In healthcare, it enables hospitals to collaboratively analyze
sensitive patient data without sharing it, improving diagnostic models [3].
Financial institutions leverage FL for fraud detection and credit scoring
while ensuring customer data confidentiality[4]. In smart devices, FL is used
to personalize recommendations and predictive text on smartphones by training
models locally. Autonomous vehicles benefit from FL by sharing model updates
among fleets to enhance object detection and driving behavior predictions. In
cybersecurity, FL aids in detecting anomalies across distributed networks, and
in the Internet of Things (IoT), it facilitates device management and
predictive maintenance [5]. Overall, FL’s ability to train models securely
makes it valuable across various industries where data privacy is crucial.

What is FL in healthcare? FL is becoming popular in healthcare due to its
application in data privacy, which is particularly important for handling
sensitive patient medical data, such as images, reports, electronic health
records (EHRs), and so on [3]. FL allows healthcare organizations to jointly
train ML models while keeping raw data localized and secure, safeguarding
patient privacy. This approach has been implemented across various healthcare
sectors, particularly in disease diagnosis [6]. Models trained on diverse,
multi-institutional datasets have improved the accuracy of detecting
conditions such as cancer, diabetic retinopathy, and other illnesses. Figure 1
illustrates the FL framework used to train various TL-based models, such as
VGG16, ResNet50, and ResNet101, across two separate client servers. The
clients exchange their trained weights, which serve as model parameters for
the global model.

Figure 1: Privacy-focused FL for medical data: This distributed framework
allows for the sharing of model weights among different clients. It assesses
generalization on decentralized data by combining traditional deep learning
with innovative decentralized techniques. This approach is used to improve
predictive accuracy and provide detailed insights into the Monkeypox outbreak
[6].

Additionally, FL has enabled the secure sharing of EHR data, fostering the
development of predictive models for patient outcomes and personalized
treatment plans while maintaining privacy standards. In drug discovery, FL
supports cross-institutional data collaboration, improving the identification
of potential therapeutic compounds. Moreover, FL has facilitated the analysis
of wearable device data, contributing to the early detection and monitoring of
chronic diseases like heart disease and diabetes. By emphasizing privacy and
security, FL encourages data collaboration while protecting sensitive medical
information [7]. Over the years, several key developments have significantly
influenced the direction of FL in healthcare [8, 9, 10]:.

  * •

Privacy-Preserving Data Sharing: FL enables healthcare organizations to
collaborate on building robust ML models without sharing raw patient data.
This is particularly important for sensitive medical information such as
images, EHRs, and genomics data. By keeping data localized and training models
across multiple institutions, FL helps maintain patient privacy while
leveraging a larger, more diverse dataset for improved model accuracy.

  * •

Enhancing Disease Diagnosis and Detection: Federated models trained on diverse
datasets from multiple healthcare institutions can detect diseases like
cancer, diabetic retinopathy, and COVID-19 more accurately. They benefit from
the shared expertise and data diversity across institutions. This
collaborative approach leads to the early detection and better diagnosis of
diseases, improving patient outcomes by providing clinicians with more
reliable diagnostic tools.

  * •

Optimizing Predictive Models for Patient Outcomes: FL supports the development
of predictive models that analyze patient histories and treatment plans to
forecast patient outcomes and personalize treatments. Hospitals and clinics
can use these predictive models to identify high-risk patients, adjust
treatment plans proactively, and reduce hospital readmission’s.

  * •

Drug Discovery and Development: FL enables pharmaceutical companies and
research institutions to collaboratively analyze data related to drug efficacy
and safety without exposing proprietary information. This approach speeds up
the identification of potential therapeutic compounds and allows for a more
comprehensive assessment of drug effects across diverse patient populations.

  * •

Wearable Device Data Analysis: FL enables decentralized analysis of continuous
data streams, helping clinicians monitor and predict chronic disease trends.
Early detection and monitoring of conditions like heart disease and diabetes
are significantly enhanced, leading to better patient management.

We organize this paper as follows: Section 2 discusses the methodology used in
our study of FL in healthcare. Section 4 provides definitions, theories, and
algorithms related to FL. Section 5 explores the types of FL. Section 6
examines misconduct threat models and adversarial goals in these systems.
Section 7 looks at FL for healthcare applications. Section 8 addresses the
challenges of implementing FL. Section 9 outlines potential future research
directions. The paper concludes with Section 10, which synthesizes our
findings and suggests avenues for future research.

Figure 2: Study selection process depicted using the PRISMA flow diagram,
including identification, screening, and inclusion steps.

##  2 Methodology

This systematic review followed the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) guidelines to ensure a transparent and
comprehensive reporting of the review process. The guidelines were employed
for identifying relevant studies, extracting data, and synthesizing findings.
By adhering to the PRISMA guidelines, this review maintains methodological
rigour, enhances reliability, and promotes the reproducibility of the
systematic review [11]. The process of paper searching, screening, and
selection followed the 2015 PRISMA guidelines, as shown in Figure 2. Initial
searches were performed on two major databases, Scopus and Science Directory
(SD) to find high-quality articles and SLRs from 2020 to 2024 [12, 13, 14,
15]. Figure 3 provides a statistical overview of papers published on FL in
various disciplines over the last five years. Figure 3(a) illustrates a
substantial increase in research interest in FL from 2020 to 2023, with the
number of publications rising from 722 to 5975. As of May 3, 2024,
approximately 2208 papers have been published in FL domains this year,
demonstrating ongoing progress across various fields. Figure 3(b) shows that
Computer Science is the predominant area, comprising 47% of FL publications.
It is followed by Engineering and Mathematics, which account for 23% and 12%
of publications, respectively. In contrast, fields such as Medicine (2%),
Business, Management and Accounting (1%), and Materials Science (2%) have seen
minimal exploration. These trends highlight a significant emphasis on the
technical and computational elements of FL, while its potential in health,
social sciences, and materials science remains relatively untapped.

Figure 3: This survey examines the most recent advancements in FL within the
field of computer science and engineering, particularly emphasizing the
healthcare sector. It presents statistics on (a) the number of papers
published in the past five years on FL and (b) the distribution percentage of
these papers across various domains. The plots illustrate a steady increase in
recent literature.

For a more targeted and relevant search, Boolean operators were employed with
the terms “Federated Learning" and either “healthcare," “disease," or
“medical." This approach resulted in identifying 443 articles from Scopus and
163 articles from SD. Filtering for journal papers, peer reviews, and English
language, the selection narrowed down to 163 articles after reviewing titles,
keywords, and abstracts. One researcher (M.S. Ali) transferred data from these
163 journal articles into Excel CSV files for further detailed analysis. Using
Excel’s tools to identify duplicates, redundant entries were removed. Titles
and abstracts of these articles were independently reviewed by two reviewers
(M.M. Ahsan and M.S. Ali). Any disagreements in article selection were
resolved through discussion and consensus. Finally, following the inclusion
and exclusion criteria depicted in Figure 2, a total of 100 papers were chosen
for qualitative synthesis.

##  3 Definitions, Theory, and Algorithms

Definitions

FL is an ML framework designed to train a model across multiple decentralized
edge devices or servers that hold local data samples, all while keeping the
data localized. This approach helps preserve privacy and reduces the necessity
of transferring large datasets. Mathematically, FL can be described as follows
[16]:

Let $\mathcal{D}_{i}$ be the local dataset of the $i$-th participant in the
federation, and $f_{i}$ represent the local learning objective for that
participant. The FL problem can be formulated as the optimization of a global
objective function $F$, which typically is an aggregation of the local
objectives, which can be expressed as:

$\min_{\theta}F(\theta)=\min_{\theta}\sum_{i=1}^{N}w_{i}f_{i}(\theta;\mathcal{D}_{i}),$

where $\theta$ represents the global model parameters, $N$ is the number of
participants, and $w_{i}$ are the weights reflecting the relative importance
or size of each local dataset $\mathcal{D}_{i}$.

Federated Learning in Healthcare

The theoretical foundation of FL in healthcare is built upon the principles of
distributed computing and privacy-preserving ML. In healthcare settings, where
data is often fragmented across various institutions and contains sensitive
patient information, FL offers a compelling solution. This approach allows for
the collaborative training of ML models across multiple healthcare providers
without requiring the exchange of raw data, thereby adhering to privacy
regulations and ethical standards [17].

In practice, it can be implemented using various ML techniques that support
model training on decentralized data. Algorithms such as Stochastic Gradient
Descent (SGD) are modified for the federated setting, where each participant
computes gradients based on their local data [18]. These gradients are then
securely aggregated to update a shared global model. This model benefits from
a diverse dataset encompassing different demographics and medical conditions,
which enhances its ability to generalize and improves its diagnostic accuracy.
For instance, an FL model could be trained to identify markers of disease
across different populations using data from multiple hospitals, each
contributing insights from their unique patient groups without compromising
patient privacy [19].

General View of the Algorithm

We provide a detailed overview of the algorithmic aspects of FL for healthcare
applications. This overview contains several key steps, such as data
partitioning, local model training, and global model aggregation. A general
overview of the typical steps involved in implementing FL in healthcare is
presented below [20]:

Input: Training datasets $D_{1},D_{2},\ldots,D_{m}$ from $m$ different
healthcare institutions, Global model $M$, Number of communication rounds $R$,
Local training epochs $E$, Learning rate $\eta$.

Output: Trained global model $M$

Data Partitioning:

  1. 1.

Partition the overall training data into $m$ subsets,
$D_{1},D_{2},\ldots,D_{m}$, where $D_{i}$ represents the local dataset of the
$i$-th healthcare institution.

Local Model Training:

  1. 1.

For each communication round $r$ from $1$ to $R$:

    1. (a)

Send the global model $M^{(r-1)}$ to all $m$ institutions.

    2. (b)

Each institution $i$ initializes its local model $M_{i}^{(r)}$ with
$M^{(r-1)}$.

    3. (c)

Each institution $i$ trains $M_{i}^{(r)}$ on its local dataset $D_{i}$ for $E$
epochs using a local optimizer with learning rate $\eta$, resulting in updated
model $M_{i}^{(r,E)}$.

Global Model Aggregation:

  1. 1.

Collect the updated models $M_{1}^{(r,E)},M_{2}^{(r,E)},\ldots,M_{m}^{(r,E)}$
from all $m$ institutions.

  2. 2.

Aggregate the local models to update the global model:

$M^{(r)}=\frac{1}{m}\sum_{i=1}^{m}M_{i}^{(r,E)}$

Prediction:

  1. 1.

Given a new example $x$, use the trained global model $M^{(R)}$ to predict the
label $y$.

  2. 2.

Return the predicted label $y$.

##  4 Types of Federated Learning

####  4.0.1 Horizontal Federated Learning

Horizontal Federated Learning (HFL) represents a distributed ML paradigm aimed
at enhancing model performance by harnessing data from decentralized devices
or nodes. This technique, often applied in scenarios involving Internet of
Things (IoT) devices or mobile devices, enables collaborative learning while
preserving data privacy and security. HFL operates through horizontal
federation, where data is partitioned horizontally across multiple nodes,
allowing each node to process its portion of the data locally. This
decentralized approach reduces the risk of data breaches and ensures that
sensitive data remains on users’ devices, addressing significant concerns
regarding privacy and data security [21, 22].

Figure 4: Horizontal FL in the healthcare sector [16].

A notable advantage of HFL lies in its ability to leverage a larger and more
diverse dataset compared to centralized ML methods. By aggregating data from
multiple nodes, HFL enables models to be trained on a comprehensive range of
data sources, leading to improved accuracy and robustness [23]. This
distributed learning approach also offers potential benefits in various
domains, including healthcare, where it allows for collaborative training on
electronic health records (EHR) from multiple institutions without
compromising patient privacy [21].

However, despite its advantages, HFL presents several challenges that need to
be addressed. Ensuring data consistency and quality across distributed nodes
is a critical concern, as biases and errors in individual node data can
significantly impact model performance [24]. Strategies such as data
preprocessing and quality checks are employed to mitigate these challenges and
ensure the reliability of the training process [25].

Table 1: Uses of Horizontal FL in the healthcare sector.

Application | Related Research Papers
---|---
Heart Disease Prediction |  | Hybrid Classifier-Based FL in Health Service Providers
---
for Cardiovascular Disease Prediction [26]
Mortality Prediction |  | FL of electronic health records to improve mortality prediction
---
in hospitalized patients with COVID-19: machine learning approach [27]
Image Analysis |
MRI Image Analysis |  | FL of generative image priors for MRI reconstruction [28]
---
CT Image Analysis |  | Blockchain-federated learning and deep learning models for
---
covid-19 detection using ct imaging [29]
fMRI Image Analysis |  | Multi-site fMRI analysis using privacy-preserving FL
---
and domain adaptation: ABIDE results [30]
Disease Detection |
COVID-19 Detection |  | Dynamic-fusion-based federated learning for COVID-19 detection [31]
---
Skin Cancer Detection |  | Multimodal melanoma detection with FL [32]
---
Clinical Decision Support |
| Diabetic Retinopathy
---
Decision Support
| Making FL robust to adversarial attacks by learning
---
data and model association [33]
| Heart Failure Decision
---
Support
| FedSDM: FL based smart decision making module for ECG data in
---
IoT integrated Edge-Fog-Cloud computing environments [34]
Natural Language Processing |
| Electronic Health Record
---
Text Analysis
| Protecting personal healthcare record using blockchain & FL technologies
[35]
---

Efficient and scalable implementation of the training process remains another
significant challenge in HFL. Training on combined data from multiple nodes
requires optimization and scaling strategies, including parallel processing
and distributed training, to manage the resource-intensive nature of the task
[36, 37]. Despite these challenges, HFL offers a promising approach to improve
model accuracy while preserving privacy in distributed environments. HFL in
healthcare systems is illustrated in Figure 4, showcasing its application and
workflow within the domain. Additionally, Table 1 demonstrates the diverse
applications of HFL in healthcare, particularly in predictive modeling. The
table highlights insights from various studies, providing a comprehensive
overview of authors’ perspectives and methodologies utilized in implementing
HFL in healthcare systems.

####  4.0.2 Vertical Federated Learning

Vertical Federated Learning (VFL) aims to enhance the efficacy of ML models by
leveraging data from diverse distributed nodes or devices [38]. This method
involves vertically partitioning the data of each node, ensuring that each
node possesses a distinct set of attributes or characteristics, and
subsequently training the model using collective data from all nodes. The
proposed approach aligns with findings reported by Pfitzner et al. [39].

VFL finds widespread application in domains such as healthcare and finance,
where heterogeneous data is commonly gathered from diverse devices and nodes
[40]. In healthcare, VFL enables the aggregation of medical records from
numerous patients, contributing unique information for model training [41].
This study proposes a hybrid ML approach for medical diagnosis, training the
model on data from all patients to expand the dataset significantly compared
to centralized ML approaches [26]. The application of VFL in the healthcare
system is visually depicted in Figure 5.

The pre-processing phase of VFL involves vertical data partitioning, dividing
data from each node into distinct subsets corresponding to specific data
categories [42]. Subsequently, the data is introduced into the model for
training [43], enhancing overall performance by learning from a larger and
more varied dataset [44].

Ensuring consistency and high-quality performance across all nodes poses a
significant challenge in VFL [45]. To address this challenge, VFL typically
implements quality control measures and data refinement techniques [46].

The practicality and scalability of training in VFL is a recognized challenge
[46, 47]. Optimization and scaling approaches, such as parallel processing and
distributed training, are commonly employed to overcome these challenges. The
study presents equations for VFL derived from the optimization problem of
minimizing the loss function while preserving data privacy [37].

Figure 5: Vertical FL in the healthcare sector [48]. Table 2: Vertical FL and
its uses in the Healthcare sector.

Application | Related Research Papers
---|---
Disease Diagnosis | Heart Disease Diagnosis |  | Communication-efficient vertical FL [49]
---
Cancer Diagnosis |  | A review of medical FL: Applications in
---
oncology and cancer research [50]
COVID-19 Diagnosis |  | Covid-19 imaging data privacy by FL design:
---
A theoretical framework [51]
Clinical Decision Support | Predicting Adverse Drug Reactions |  | FL for healthcare domain-Pipeline, applications
---
and challenges [52].
Diagnosis Support for Rare Diseases |  | FL for healthcare domain-Pipeline, applications
---
and challenges. [52].
Electronic Health Record Analysis | Clinical Outcome Prediction |  | FL approaches for fuzzy cognitive maps to support
---
clinical decision-making in dengue [53].
Imaging Analysis | MRI Image Segmentation |  | Review on security of FL and its application in
---
healthcare [54].
PET Image Analysis |  | A multi-modal heterogeneous data mining algorithm using
---
federated learning [55].

Vertical FL represents a distributed ML technique that leverages collective
data from decentralized devices or nodes, enabling the development of more
precise models while safeguarding user privacy [56, 57]. This methodology
allows for training on a significantly more extensive and diversified dataset
compared to conventional centralized ML approaches [58], offering promising
implications for various domains, including healthcare and finance. Table 2
illustrates the diverse applications of VFL in healthcare, showcasing its
predictive modeling capabilities alongside insights from various studies on
the topic.

####  4.0.3 Federated Transfer Learning

The objective of Federated Transfer Learning (FTL), an ML technique, is to
ensure confidentiality while transferring information from a centralized model
to a decentralized one [59]. Fan et al., in their work on IoT Defender [60],
suggest an approach where a centralized model is trained first, followed by
the transmission of acquired knowledge to decentralized models trained on
smaller datasets. That approach aims to enhance the performance and precision
of distributed models by assimilating knowledge from the centralized model.

Figure 6: Federated Transfer Learning in the healthcare sector [52].

FTL has emerged as a promising technique for training more precise models
while preserving individual privacy [61]. By leveraging knowledge learned from
one domain to another, FTL improves model performance in new domains while
safeguarding individual data privacy. This approach is particularly relevant
in scenarios with sensitive or distributed data, minimizing the risk of data
breaches and unauthorized access [62]. The application of FTL has become
prevalent in IoT and mobile device environments, addressing limitations posed
by distributed nodes or devices with limited resources. This approach
facilitates secure and privacy-preserving learning processes by eliminating
the need for centralized data storage [63].

Zheng, Xiao, et al. explored the efficacy of FTL in knowledge transfer across
domains, utilizing strategies such as fine-tuning, knowledge distillation, and
model compression [64]. In other studies, the authors examine the relationship
between knowledge distillation and model compression, both involving the
central model, to facilitate training of distributed models and enable local
training [65, 66]. Additionally, Zhang, Wei, et al. present a comprehensive
analysis of equations for FTL, exploring the mathematical formulations
governing knowledge transfer in federated learning. Their analysis underscores
the importance of problem-specific settings and the nature of data in FTL
equations [67].

Figure 6 visually illustrates the application of FTL in healthcare, providing
insights into its functionality and operation within the healthcare domain.

####  4.0.4 Federated Domain Adaption

Figure 7: Federated domain adaption in the healthcare sector [24].

Federated Domain Adaptation (FDA) is a well-established ML technique widely
applied across various domains. FDA facilitates knowledge transfer between
domains while safeguarding user privacy and addressing the challenge of domain
shift. The method has been extensively documented in previous research,
demonstrating promising results, particularly in smart systems [68].
Leveraging domain adaptation algorithms, FDA aligns distributions between a
source domain, typically data-rich, and a target domain, often characterized
by limited data and annotations [69]. By reducing the divergence between
domains, the FDA enhances model generalizability, especially in scenarios with
sparse target domain data [70]. Figure 7 visually illustrates the FDA’s
application in healthcare, providing insights into its functionality within
this domain.

Utilized where data centralization is impractical or undesirable, FDA enables
the development of precise models while preserving data confidentiality [71].
This approach strategically utilizes existing data to train models suitable
for target domains with limited data and distinct distributions from source
domains [72].

The adaptation process involves adjusting a pre-trained model from the source
domain to fit the target domain’s data distribution, ensuring sensitive data
remains on users’ devices [73]. The updated model parameters are then merged
to create a global model tailored for the target domain [74]. The FDA
facilitates knowledge transfer across domains while preserving privacy,
benefiting scenarios with limited target domain data and distinct
distributions.

####  4.0.5 Multitask Federated Learning

Multitask Federated Learning (MTFL) enables the training of multiple models in
parallel using distributed data, where each model is dedicated to a unique
task [75]. These models collaborate to learn from each other, improving
overall performance as a collective unit [76]. MTFL has gained prominence in
scenarios involving interrelated activities, where data is dispersed across
various devices or nodes [77]. By leveraging contextual knowledge from
different tasks, MTFL enhances model training precision and overall system
performance [78]. This technique is particularly advantageous when data
decentralization is preferred or necessary [79]. Figure 8 illustrates the
application of MTFL in healthcare, showcasing its functionality within this
domain.

Figure 8: Multitask FL in the healthcare sector [80].

In MTFL, models are iteratively trained, with local parameter updates
consolidated at a central node [81]. This allows each device’s model to
benefit from aggregated parameters, improving training efficacy [82]. MTFL
optimizes joint learning across multiple tasks in a federated setting and
ensures privacy and security while achieving high accuracy across tasks [83].
The objective function minimizes the aggregate loss of functions across tasks
and devices, with global parameters updated iteratively [84].

####  4.0.6 Federated Meta Learning

Federated Meta-Learning (FML) aims to develop models capable of swiftly
adapting to new challenges by leveraging decentralized data sources and prior
knowledge from diverse tasks [85, 86]. In FML, models are iteratively trained
on multiple tasks, adjusting their parameters based on acquired knowledge, and
then applied to new tasks with revised parameters [87, 88].

This approach offers an expedited adaptation to novel tasks without the need
for retraining from scratch, saving time and resources [89, 90]. Additionally,
FML addresses the challenges posed by distributed data, enabling model
training across multiple devices without centralized data repositories [91,
92, 93]. FML optimizes meta-models concurrently across devices, fine-tuning on
local tasks while utilizing shared meta-models to acquire task learning
knowledge [94, 95]. This optimization facilitates rapid adaptation to new
tasks with limited data, ensuring data confidentiality while minimizing meta-
model loss across validation tasks [96, 97].

Figure 9: Federated Meta Learning in health care sectors [98].

Moreover, FML trains task-agnostic models with adaptability and versatility,
efficiently aggregating decentralized data to develop models capable of
performing well across diverse tasks or domains [99, 100]. Figure 9
illustrates FML’s application in healthcare, demonstrating its functionality
within this domain.

##  5 Model Misconduct

The present study delves into the phenomenon of model misconduct in the
context of FL in healthcare. Specifically, the term "model misconduct" is
defined as the deliberate or inadvertent introduction of errors, bias, or
other inaccuracies into the ML models developed through the FL approach. This
phenomenon has been previously discussed in the literature [101]. The
phenomenon of divergent model performance, whether deliberate or inadvertent,
can be attributed to many factors, including dissimilarities in data quality
or variations in the selection of algorithms or parameters during the training
phase. The occurrence of model misconduct has been identified as a potential
source of concern in the medical field, as it may result in erroneous
diagnoses, suboptimal treatment outcomes, and other unfavorable consequences
for patients. Identifying and remedying model misconduct in FL within the
healthcare domain is paramount to guarantee the developed models’ precision
and impartiality.

###  5.1 Data leakage

FL allows organizations to collaborate on model development without sharing
sensitive data, ensuring privacy by enabling local training and aggregating
model updates instead of raw data, thus maintaining confidentiality [102]. The
healthcare industry is attracted to FL for its ability to protect highly
sensitive patient data. However, it’s essential to recognize the risk of data
leakage in FL, which could compromise patient privacy and violate data
protection regulations [103].

For instance, Chang et al. address data leakage attacks in FL by proposing two
defence methods: gradient sparsification and pseudo-gradient techniques. Both
methods use cosine similarity to measure gradient differences, and disguise
uploaded gradients, protecting privacy without sacrificing model accuracy.
Extensive experiments show that these methods effectively safeguard private
data while maintaining model performance. However, the effectiveness of these
defence methods may vary with different data characteristics and training
scenarios, requiring careful parameter tuning [104].

Data leakage, characterized by inadvertent or malicious disclosure of
sensitive data to unauthorized parties, poses a significant threat in FL. It
can occur during data transmission if communication channels are insecure or
if transmission software or hardware vulnerabilities exist. Additionally,
insecure storage of data on servers or devices can lead to data leakage if
compromised by hackers or malicious actors. Flaws in FL algorithms may also
contribute to data leakage if they inadequately protect patient privacy.

To mitigate data leakage risks in healthcare-related FL, organizations should
implement security measures such as encryption, authentication protocols,
secure storage devices, and privacy-preserving mechanisms in algorithms.
Regular monitoring for vulnerabilities and prompt response are crucial to
ensuring patient data remains secure throughout the FL process [105].

###  5.2 Model poisoning

Model poisoning is a cyber-attack that poses a significant threat to the
accuracy and reliability of ML models, including those utilized in FL [106].
In healthcare, model poisoning attacks can have severe implications,
jeopardizing patient safety and privacy. This type of attack involves an
attacker intentionally introducing malicious data into the training dataset to
manipulate the behavior of the ML model. The injected data is carefully
crafted to bias the model in favor of the attacker, leading to inaccurate or
unreliable predictions. By tampering with the training data, the attacker aims
to compromise the integrity and effectiveness of the model, potentially
causing harm in real-world applications [107]. Detecting model poisoning
attacks in FL can be particularly challenging due to the distributed nature of
the training data across multiple healthcare organizations, making it less
visible to a central authority [108].

###  5.3 Model Stealing

Model stealing attacks involve an attacker replicating an ML model by
analyzing its outputs through queries and capturing responses. In FL, this
poses a risk for healthcare due to the distributed data across multiple
organizations, limiting central authority. Protecting against model stealing
requires robust security measures to safeguard sensitive healthcare data and
prevent unauthorized model replication [109].

Sun et al. propose an intrusion detection system (IDS) using Particle Swarm
Optimization (PSO) and AdaBoost to detect malware in health app platforms.
Using the NSL-KDD dataset, PSO selects 12 relevant features, and AdaBoost
achieves a recall value of 0.97. Integrating ML-IDSs into health apps enhances
patient care, reduces costs, and secures medical data. This study highlights
the potential of ML-IDSs in improving the security of medical IoT devices
within the Internet of Medical Things (IoMT) [110]. However, the approach
relies on high-quality input data and assumes the availability of relevant
data, which may not always be available. Its dependence on selected features
may limit its application where features do not fully capture attack
characteristics.

Differential privacy is a technique that can help mitigate the risk of model
stealing by adding random noise to the training data to obscure individual
data points. This can make it more difficult for attackers to reconstruct the
original data set and reverse-engineer the ML model.

Model watermarking involves adding a unique identifier to the ML model that
can be used to detect whether it has been stolen. This can be done by
modifying the weights or structure of the model in a way that is imperceptible
to its performance but leaves a distinctive pattern that the original owner
can recognize. In addition to these techniques, healthcare organizations must
implement strong security measures to protect their ML models from
unauthorized access and theft. This can include using strong encryption and
authentication protocols for data transmission, regularly monitoring the
system for potential security vulnerabilities, and promptly addressing any
issues.

###  5.4 Biased Models

Biased ML models present a significant risk within FL for healthcare,
potentially leading to incorrect diagnoses and inappropriate treatments [111].
Kairouz, Peter, et al. scrutinize biased models in FL for healthcare,
proposing mitigation strategies and assessing their impact on model
performance. Their study delves into various sources of bias and explores
methods to address and mitigate bias within the healthcare-focused FL
framework [112]. Inadequate or biased training data, decentralized data
distribution, and algorithmic limitations can contribute to bias in ML models.
Effectively ensuring representativeness and mitigating bias within FL for
healthcare require robust strategies tailored to the decentralized data
landscape. Additionally, addressing algorithmic biases is crucial for
maintaining model fairness and accuracy in healthcare FL scenarios.

Zhang et al. (2024) utilized robust optimization to enhance FL models,
ensuring their consistent performance and fairness across diverse
demographics. Their study introduced a unified framework aimed at healthcare
institutions, incorporating various fairness metrics and implementing this
through an efficient collaborative ML approach. The proposed framework,
Federated Learning with Unified Fairness Objective (FedUFO), was tested across
four digital medical scenarios in a federated context, demonstrating its
ability to preserve model performance while improving fairness. The federated
results, particularly for the COVID-19 dataset, showed an overall accuracy of
66.24%, which underscores the framework’s effectiveness in real-world settings
[113]. However, while the framework aims to address and unify diverse levels
of fairness in FL, the dependence on collaborative participation from multiple
healthcare institutions might introduce variability in adherence to the
fairness metrics, potentially impacting the overall efficacy of the model.

###  5.5 Incentive Misalignment

In FL for healthcare, incentive misalignment poses a significant challenge,
potentially leading to organizations or individuals prioritizing their
interests over the goal of enhancing patient outcomes [114]. Multiple
organizations involved in FL for healthcare may have divergent goals and
objectives, complicating collaboration and data sharing [115]. For instance,
hospitals may prioritize patient outcomes, while pharmaceutical companies may
focus on drug development. Similarly, individuals within these organizations
may harbor distinct incentives; for example, physicians may prioritize patient
privacy, while researchers may prioritize academic publication. Establishing
clear incentives and rewards, such as financial incentives or recognition, can
foster collaboration and mitigate the effects of incentive misalignment in FL
for healthcare.

##  6 Misconduct Threat Models and Adversarial Goals

When multiple devices collaborate to construct a predictive model without
disclosing private data, it is referred to as FL [116]. Given the varied trust
levels of the involved devices and the potentially sensitive nature of the
data, ensuring the security and privacy of FL is paramount [117].

Both homogeneous and heterogeneous FL present unique opportunities for
malicious activity and have diverse adversarial objectives. In a homogeneous
FL scenario, where all devices share a similar data distribution, adversaries
aim to reduce the model’s reliability by interfering with individual device
updates. This can be achieved through model poisoning attacks or tampering
with the training data [118].

Data manipulation is a prevalent threat model in homogeneous FL, where
adversaries can influence the global model by tampering with training data at
the local level. For instance, injecting hostile examples into the training
data by a rogue device can lead to inaccurate predictions by the global model
[119]. Another security risk involves injecting noisy gradients into the
gradients sent by each device to the centralized server during FL training.
This malicious action can degrade the precision of the global model [120].
Homogeneous FL also faces threats from model poisoning attacks, where
adversaries adjust their local updates to undermine the global model [121].
For example, a malicious entity may tamper with the model’s parameters to
introduce bias, resulting in incorrect predictions in certain cases. In
contrast, participating devices in heterogeneous FL have diverse data
distributions, and adversaries may have varied motivations [122]. For
instance, a device with a skewed data distribution may attempt to incorporate
that bias into the overall model. Similarly, a device may choose to conceal
certain data by either omitting its input or introducing intentional faults.
Ensuring the security of individual data is a significant challenge in FL
across different types of devices [123]. Given the potentially sensitive
nature of the collected data, some devices may be reluctant to share it. To
address this, certain devices may opt to encrypt their local updates,
rendering them unreadable by the central server. However, inadvertent or
intentional errors introduced into the encrypted updates by the device may
adversely impact the overall accuracy of the model [124]. Table 3 displays the
Misconduct Threat Model along with its adversarial goals identified in various
research studies.

Table 3: Misconduct threat model and adversarial goals of FL in healthcare.

| Misconduct Threat Model
---
Adversarial Goals | Study
Data poisoning |  | Inject biased/corrupted data or modify existing data to mislead the learning process
---
or infer patient identities.
| [125]
---
Model inversion |  | Infer sensitive patient information by exploiting system vulnerabilities,
---
compromising privacy and confidentiality.
| [126]
---
Membership inference |  | Determine if a patient’s data was used in the learning process, violating
---
privacy and confidentiality.
| [127]
---
Byzantine attacks |  | Disrupt the learning process by sending incorrect or malicious updates, leading
---
to incorrect model outputs or system failure.
| [128]
---

###  6.1 Data Disruption

FL operates on a decentralized data distribution model, where data is
dispersed across multiple devices or participants instead of being centralized
in a single location, as seen in traditional ML methods. This distribution
offers various advantages. Firstly, it enhances data privacy by allowing
sensitive information to remain on individual devices, thus safeguarding it
against unauthorized access. Secondly, decentralization ensures system
resilience and security by eliminating a single point of failure. Lastly, FL
exhibits scalability, as it can efficiently handle large datasets without
necessitating centralized data storage solutions.

Table 4: Summary of public medical datasets used in recent FL studies for
medical algorithm benchmarking.

Dataset | Description | Study
---|---|---
MIMIC-III |  | A publicly available dataset containing de-identified electronic health records of over
---
40,000 critical care patients from a US hospital system.
| [27]
---
NIH Chest X-ray |  | A dataset of chest radiographs labeled for the presence of pneumonia, tuberculosis,
---
and other diseases.
| [129]
---
BraTS |  | A dataset for brain tumor segmentation that includes multimodal magnetic
---
resonance imaging scans of brain tumors.
| [130]
---
Criteo |  | A dataset used in advertising for predicting click-through rates on ads.
---
Used as a proxy dataset for predicting mortality in medical applications.
| [27]
---

In FL, participants train local models on their respective data and share
model parameters with a central aggregator, consolidating these parameters to
construct a global prediction model. However, achieving optimal model
performance in FL relies heavily on maintaining balanced data distribution
among participants. Imbalanced data distribution can hinder model
generalization and compromise its accuracy. Therefore, careful data
distribution management is crucial to ensure representative sampling and
robust model training.

Moreover, FL algorithms must account for the heterogeneity of data
distribution across participants. This necessitates the adaptation of
algorithms to accommodate varying data distributions, including differences in
missing values, outliers, or other anomalies present in the data. By
addressing these considerations, FL can effectively harness the collective
knowledge of distributed data sources while mitigating the challenges posed by
diverse data distributions (see Table 4 for a summary of public medical
datasets in recent FL studies applied for algorithm benchmarking).

###  6.2 Data Privacy and Protection

Data privacy has become a major concern in the era of big data. Numerous
security measures and encryption methods have been developed to safeguard
sensitive information. These security measures rely on the principle that only
individuals with secret keys can access sensitive data. In federated networks,
medical data must be stored locally or in the cloud. Hospitals may require on-
site or remote computing resources and PACS-type software for data preparation
and standardization. However, setting up local centers with data centers and
hardware, such as graphics processing units (GPUs), presents challenges such
as high computing power requirements, compatibility issues with other clients,
and the need for high-performance bandwidth and connections between multiple
centers. In medical centers, meeting these requirements may not always be
feasible due to inadequate internet connectivity and computational resources
[131].

Additionally, redundant computing infrastructure and data centers must be
designed for the entire network to function correctly and prevent data loss.
The network should be resilient enough to continue training even if one
computational client fails. Federated models must prioritize network
robustness to protect the privacy of models and patient data during client
changes or fluctuations in data quantity. This ensures data privacy and model
integrity regardless of network dynamics. With the widespread adoption of ML,
particularly centralized ML, data collection and transfer to a central
location are necessary to train effective models. However, this poses the risk
of data leakage for private and sensitive data. Thus, a crucial challenge in
sharing intelligence is performing ML on private datasets without compromising
data privacy. Multi-party ML with privacy protection mechanisms can assist
users in jointly learning from each other’s data while ensuring the security
of their own data. FL is a prominent example that can help address the privacy
concerns associated with multi-party computation.

Table 5: Related research studies for Medical data properties in FL for medical applications, consisting of data partitions, data distribution (i.e., non-IID) characteristics, possible data privacy attacks, and data privacy protections. Property | Study
---|---
Data partitions |  | [132, 130]
---
Data distribution (non-IID) characteristics |  | [133]
---
Data privacy protections |  | [54]
---

Related research studies have explored critical aspects such as data
partitions, non-IID characteristics, potential data privacy attacks, and
corresponding data privacy protections, as summarized in Table 5.

###  6.3 Data Privacy Attacks on ML

ML data privacy attacks involve unauthorized attempts to access, steal, or
alter private information used for training or testing ML models. These
attacks can seriously impact individuals and organizations reliant on ML
systems, compromising data security, integrity, and availability. Examples of
ML data privacy assaults include:

####  6.3.1 Data Poisoning

To bias the final model in their favor, an attacker may inject malicious data
into a training dataset, a technique known as data poisoning. For instance, an
attacker might introduce false data into the training dataset used to train
the filter to enhance the chances of their spam messages being classified as
legitimate by a spam filter.

####  6.3.2 Model Inversion

It’s a technique where an attacker tries to reconstruct private information,
such as an individual’s medical records, by exploiting the output of an ML
model. By repeatedly testing the model with carefully chosen inputs, the
attacker can glean insights into the training data used to construct the
model. Membership inference, as an attack method, aims to ascertain whether an
individual’s data was included in the training set of an ML model. Through
analysis of the model’s responses to different inputs, the attacker can deduce
the presence or absence of specific data, posing risks to privacy and
anonymity.

####  6.3.3 Model Extraction

In this method, the attacker seeks to pilfer an ML model by interrogating it
with carefully crafted inputs and leveraging the responses to replicate the
model. This could pose significant risks, particularly if the model contains
sensitive or confidential information.

####  6.3.4 Membership inference attacks

Membership inference attacks seek to ascertain whether a particular
individual’s data was part of the training dataset for an ML model [134]. To
carry out these attacks, one needs to train a separate ML model that can
identify if a specific data point contributed to the target model’s training
process [135]. Such attacks exploit the use of personal data in the model’s
training phase to deduce private information about individuals [25].

####  6.3.5 Adversarial examples

Inputs that have been altered in such a way as to cause an ML model to
generate an inaccurate prediction are referred to as adversarial examples
[33]. For these assaults to succeed, imperceptible perturbations are first
added to the data being used as input. This causes the model to classify the
data incorrectly. The reaction of the ML model to the adversarial example can
be used to infer information about the original input, which enables sensitive
information to be extracted from ML models and utilized for malicious
purposes.

####  6.3.6 Duties and skills of an attacker

In ML privacy attacks, perpetrators may adopt various personas and utilize
diverse tools to compromise the privacy of individuals or organizations.
Attackers can eavesdrop on data being transmitted between parties to gather
sensitive information, either by intercepting data in transit or exploiting
compromised devices. They may also attempt to obtain the model weights or
reverse-engineer the technique to acquire the ML models used by an
organization. Even without explicit data access, attackers may try to
determine if specific individuals’ data was used in training ML models [136].
Additionally, based on the outcomes of ML models, attackers may reverse-
engineer input data samples, potentially revealing sensitive information. Some
attackers create adversarial examples, crafted input data intended to deceive
ML models, altering outputs or extracting private data. Using differential
privacy methods, attackers may attempt to infer sensitive information from
anonymized datasets. These are just a few tactics attackers may use to
compromise ML system privacy. Organizations must be vigilant and take measures
to protect their data and models. Safeguarding training and evaluation data
through encryption and access restrictions is crucial to defend against ML
data privacy attacks. Thorough planning and testing of ML models are also
essential to identify and mitigate potential vulnerabilities.

###  6.4 Data Privacy Protection for Federated Learning

FL in ML enables collaborative model development among multiple users without
sharing sensitive raw data, ensuring privacy and cooperation. Participants
train a model on their local devices using their own data, and only model
updates are sent to a shared server, preserving data privacy and facilitating
collaborative model development. This strategy, keeping data on local devices
or servers, helps protect the privacy of sensitive data [137, 109]. Data
privacy protection in FL involves a range of strategies and procedures to
prevent the disclosure of sensitive information. One key method is
differential privacy, which obscures sensitive data to prevent attackers from
deducing it, thereby safeguarding private information [138]. Secure
aggregation enables parties to combine model updates without revealing
individual contributions, ensuring privacy while facilitating collaboration
[139]. FTL improves model precision by leveraging pre-trained models, reducing
the need to distribute sensitive data among parties. Privacy-preserving ML
algorithms, such as secure decision trees and k-means clustering, protect data
confidentiality effectively. Data access control manages who can access what
data, restricting access to private information and ensuring only authorized
users can access it [140].

##  7 Federated Learning for Healthcare Applications

###  7.1 COVID-19 Detection

FL has been effectively employed in the context of COVID-19 to aid in virus
detection using wearable technology and various medical tools [141, 142]. For
instance, smartwatches and activity trackers can monitor COVID-19 symptoms by
gathering data on temperature, heart rate, and other vital indicators,
facilitating early detection of the virus. However, collecting such vast
amounts of information from millions of devices globally poses challenges and
raises concerns regarding centralized databases, particularly regarding
privacy and security. FL addresses these challenges by allowing data to be
stored locally on devices and models to be trained in a decentralized manner.
This approach shows promise in leveraging COVID-19 EHR data to develop
accurate predictive models while safeguarding patient privacy [27]. Another
application is the detection of COVID-19 through X-ray imaging, a commonly
used method in diagnosing respiratory disorders. Salam et al. developed ML
models, including an FL model, using chest X-ray (CXR) images from COVID-19
patients, showcasing the potential of FL in this domain [143]. By keeping the
images locally stored at hospitals and clinics, FL maintains privacy and
security while enabling model training on a sizable dataset [144].
Furthermore, FL can be utilized in COVID-19 detection through CT scans, where
models can be trained on a large dataset of CT images to identify distinctive
patterns associated with the virus. The approach proposed by Rajesh et al.,
combining blockchain technology and FL for joint model training, ensures
anonymity while enhancing COVID-19 detection accuracy [29]. Overall, FL offers
a valuable method for enhancing COVID-19 detection accuracy and speed while
preserving data confidentiality and security amidst the increasing utilization
of wearable technology and medical equipment.

###  7.2 Breast Density Classification:

Classifying breast density is crucial for assessing a patient’s risk of
developing breast cancer. Modern technology called FL offers a promising
avenue to enhance breast density classification. With FL, several parties can
collaborate decentralized while maintaining the confidentiality and privacy of
their data [145]. To train an ML model for classifying breast density, several
hospitals or imaging facilities may pool their data while keeping the
identities of their separate datasets a secret. The lack of extensive, high-
quality datasets that adequately represent the broad community of mammography-
using women is one of the main obstacles to breast density classification.
Using an FL method, the open-source software MammoDL employs the U-Net DL
architecture to quantitatively analyze the density and complexity of breast
tissue from mammograms [146]. By combining smaller datasets from several
institutions into a more extensive, more diversified dataset, FL can aid in
overcoming this difficulty [147]. This additional diversity can enhance the ML
model’s performance and lower the danger of overfitting, which happens when a
model is trained too closely on a particular dataset and struggles to
generalize to new data. Additionally, FL may help with privacy issues. All
data must be centralized in a standard ML scenario, making it susceptible to
hackers and data breaches. In FL, the raw data is still stored on the servers
of the individual universities and is only utilized to update models. Patient
privacy is safeguarded, and the risk of data leakage is reduced.

###  7.3 Healthcare Monitoring

FL promises a transformative impact on healthcare monitoring by enabling
collaboration among academia and healthcare professionals [8]. By pooling data
from diverse sources like medical devices, patient records, and wearable
technology, FL facilitates the development of accurate ML models to forecast
patient health outcomes [63]. These models predict disease susceptibility,
readmission risks, and medication responses using data from electronic health
records, medical devices, and patient-generated data. Wearable devices, such
as fitness trackers, can remotely monitor vital signs, activity levels, and
sleep patterns, alerting healthcare providers to any deviations. Proposed
frameworks, like edge-assisted data analytics, employ FL to locally retrain ML
models using user-generated data while preserving privacy [148]. FL ensures
patient data privacy by retaining data locally and encrypting it before
transmission to a central server, reducing the risk of data breaches and
unauthorized access [138]. Overall, FL holds the potential to revolutionize
healthcare monitoring by improving forecast accuracy, providing personalized
care, and safeguarding patient privacy.

Dheeba et al. (2024) explore a decision support system for heart disease
prognosis, deploying it on both cloud-native and edge-optimized models. The
system, named ClassifyIT, employs a custom neural network architecture called
IPANN, a feature selector named MIST-CC, and a regularizer named STIR.
ClassifyIT achieved an accuracy of 87.16% on the Cleveland dataset,
outperforming a regular deep network’s 78.80%. Adding MIST-CC improved the
accuracy to 81.97%, and including STIR further enhanced it to 85.54% [149].
The study introduces two deployment strategies: cloud-native and edge-
optimized. The cloud-native model offers a centralized, scalable solution,
while the edge-optimized model improves scalability by shifting computation to
user devices. The ML pipeline is further refined using FL, which promotes
localization and collaborative learning. The edge-optimized architecture
ensures better scalability and adaptability for real-world applications.
However, potential limitations of the proposed methods include data
availability and integration across cloud and edge platforms. Moreover, the
system’s reliance on FL demands robust infrastructure and coordination among
multiple devices. Additionally, it is unclear whether their proposed
approaches are user-friendly.

###  7.4  Medical Imaging

FL in healthcare, particularly in medical imaging, involves using ML
techniques to process medical images decentralized [150, 151]. This approach
is vital for healthcare because medical imagery is crucial in diagnosing and
treating patients. Medical imaging data is typically collected from various
sources like imaging facilities, hospitals, and clinics, each with unique
datasets. FL allows these entities to collaborate in training global models,
which improves disease identification and treatment effectiveness over time as
more data becomes available [63]. FL addresses privacy concerns inherent in
centralized data systems by keeping data secure and private while leveraging
collective knowledge from multiple organizations to enhance model development
[152]. FL algorithms, such as Federated Averaging (FedAvg), enable real-time
collaboration among organizations, accelerating model development and disease
diagnosis [153]. FL also addresses data imbalance issues common in medical
imaging by pooling data from diverse sources, resulting in more robust models
and accurate diagnoses [154, 155]. Overall, FL promises to improve patient
outcomes and reduce healthcare costs in medical imaging applications. Various
publications have explored the applications of FL in medical contexts,
summarizing their findings, methodologies, and outcomes, as shown in Table 6.

Table 6: Summary of FL publications applied in medical applications.

ML Task | Clinical Tasks | Medical Input Data | Model Architecture | Related FL Study
---|---|---|---|---
Classification |  | COVID-19
---
Diagnosis
| Chest X-ray
---
images
CNN, ResNet-18 |  | [156]
---
Classification |  | Diabetic Retinopathy
---
Diagnosis
Fundus images | CNN, MobileNet-v2 |  | [157]
---
Segmentation |  | Brain Tumor
---
Segmentation
MRI scans | CNN, U-Net |  | [158]
---
Segmentation |  | Cardiac MRI
---
Segmentation
MRI scans | CNN, U-Net |  | [159]
---
Segmentation |  | Lung Tumor
---
Segmentation
CT scans | CNN, U-Net |  | [160]
---
Regression |  | Mortality
---
Prediction
| Electronic Health
---
Records
LSTM, MLP |  | [161]
---
Regression |  | Blood Pressure
---
Prediction
PPG signals | LSTM |  | [162]
---
Regression |  | Heart Failure
---
Prediction
| Electronic Health
---
Records
Multi-task LSTM |  | [163]
---

For instance, Joynab et al. (2024) address cervical cancer detection using
CNN-based FL architectures, balancing accurate image classification and data
privacy across three experimental settings. The proposed system aggregates
updates from locally trained models into a global model, achieving test
accuracies of 94.36% in an IID setting and 78.4% in a non-IID setting [164].
However, the model’s performance varies significantly between IID and non-IID
settings, highlighting potential challenges with heterogeneous data. The
reliance on FL also requires robust infrastructure and cooperation among
institutions, which may not always be feasible.

Truhn et al. (2024) address privacy concerns in AI training for cancer image
analysis using Somewhat-Homomorphically-Encrypted Federated Learning (SHEFL).
This method transfers only encrypted weights, avoiding data breaches during
model updates. SHEFL was successfully implemented in various cancer image
analysis tasks using multicentric datasets. For instance, SHEFL models
achieved an 80.32% Dice score, close to the 81.71% of FL models [165].
However, the computational overhead of homomorphic encryption can slow down
training. Additionally, while SHEFL improves privacy, it does not entirely
eliminate risks. The method also depends on secure infrastructure and
collaboration between institutions.

Ahsan et al. (2024) proposed models such as M-VGG16, M-ResNet50, M-ResNet101,
and ViT for classifying Monkeypox using image analysis. The M-VGG16 achieved
88% accuracy on a small dataset and 76-77% on an imbalanced dataset. The
M-ResNet50 performed best with 89% accuracy on a multiclass dataset. Models
trained with Adam surpassed those trained with SGD. The study highlights the
use of FL to improve AI-based diagnostic models by enabling collaborative
model training without data sharing, thereby preserving patient privacy. The
integration of FL demonstrates the potential for developing robust and secure
healthcare solutions [6]. However, the study faces several limitations,
including a limited number of Monkeypox images, the need for expert
verification, testing on highly imbalanced data, and the development of mobile
diagnostic tools.

###  7.5 Electronic Health Record

FL has the potential to enhance clinical decision-making within EHR systems
significantly [166]. Initially proposed by Google for board question
suggestion, FL involves training a global model using data from various
sources, including wearable technology, hospital systems, and individual
health records [167]. Each participant’s device trains the global model
locally, with parameters then aggregated to update the global model. FL
addresses the issue of data silos in EHR systems, allowing insights and
predictions based on a broader and more diverse dataset without moving the
data [168].

Kuliha et al. (2024) address the significant role of EHRs in healthcare,
focusing on the challenges of data management and security through FL and
blockchain technology. They tackle issues like motivating participation in FL,
ensuring accurate model aggregation, and managing the large volume of EHR
data. Their proposed solution integrates blockchain with cloud solutions to
enhance interoperability, albeit at the cost of compromising the immutability
of EHRs [169]. However, the integration of blockchain could lead to potential
delays in updating EHRs due to additional validation steps.

Pan et al. (2024) propose an adaptive FL framework to address data
distribution drift across institutions in clinical risk prediction using EHR.
This framework separates input features into stable, domain-specific, and
conditional-irrelevant parts based on their relationship to clinical outcomes.
Evaluated on a large-scale intensive care unit (ICU) dataset, the framework
predicts the onset risk of sepsis and acute kidney injury (AKI) more
effectively than existing FL baselines, while also being clinically
interpretable.

The experimental results demonstrate that the proposed method outperforms
other FL approaches, with notable AUROC scores for AKI and sepsis prediction
across multiple hospitals. For example, the AUROC for AKI prediction using the
proposed method ranged from 0.679 to 0.818 across hospitals, surpassing other
methods like APPLE and RMTL. For sepsis prediction, the proposed method
achieved AUROC scores from 0.659 to 0.861, again outperforming other
techniques [170]. However, the study did not mention whether their proposed
method can handle the computational complexity or how their proposed method
handles the data variability for clinical practices across various
institutions

Some propose combining Deep Learning (DL) and blockchain technologies to
enhance privacy protection in EHR, using techniques like anomaly detection and
cryptography-based FL modules [168]. Despite its potential benefits,
integrating FL into EHR faces challenges such as data privacy, security, model
fairness, accuracy, and legal and ethical concerns. Addressing these obstacles
is crucial to ensure patient data confidentiality and privacy while leveraging
FL to improve clinical decision-making.

##  8 Challenges of Federated Learning

While FL offers the potential for multiple devices to collaborate on training
ML models without sharing raw data, it also presents several challenges that
need to be addressed [112]. Figure 10 illustrates a framework of challenges
associated with FL. These significant challenges include:

###  8.1 Heterogeneity of Data and Devices:

The variety of data and devices makes deploying FL effectively in the
healthcare industry challenging.

  * •

Data Heterogeneity: Healthcare data exists in diverse formats such as text,
images, and time series, each with unique characteristics requiring specific
processing methods for model training. Variations in data quality among
devices, influenced by device specifications, sensor accuracy, and patient
demographics, pose challenges. Therefore, data pre-processing and
standardization are essential to ensure the success of FL.

  * •

Device Heterogeneity: Healthcare devices vary in hardware and software
requirements, network connectivity, and power consumption. Some devices may be
resource-constrained and lack computational or memory capabilities to
participate in FL. Additionally, disparities in operating systems or
programming languages among devices require additional data interoperability
and communication efforts. Various strategies have been devised to address
this heterogeneity in FL so that healthcare can overcome differences in data
and devices.

  * •

Federated Transfer Learning: This method uses pre-trained models on related
data domains to speed up model training on heterogeneous devices. Algorithms
for adaptive learning can modify the model parameters by the device’s
processing power. Employing efficient communication protocols to decrease data
transfer and lower device computing load is possible.

###  8.2 Data privacy and security:

Even though this strategy has several benefits, including enhanced privacy and
lower communication costs, it also presents severe problems for data
protection [171]. The following are a few of the significant issues with data
privacy that FL presents:

  * •

Leakage of data: In FL, devices distribute updates to the model, but these
changes could still include private information about the local data [172,
173]. Potentially, attackers can intercept these updates and deduce private
information about the local data. Encryption and other privacy-protecting
methods must be used to stop data leakage [174].

  * •

Model inversion attacks: This privacy problem is also possible in FL. These
attacks use the model updates to recreate the initial training data utilized
by the local devices [175]. It is essential to put privacy-preserving
procedures that safeguard the local data to stop these assaults.

  * •

Attacks using membership inference: Attackers may be able to identify which
device was used to train the model in FL if they have access to certain
devices. The device’s user or the local data may be revealed via this
information in sensitive ways [176]. Employing acy-preserving methods like
differential privacy can stop membership inference attacks [177].

  * •

Attacks on the central server: In FL, the central server gathers model updates
from the local devices. An attacker may gain access to all updates and deduce
vital information about the local data if the primary server is hacked [178].
Therefore, it is essential to put in place robust security measures to
safeguard the central server.

Figure 10: Challenges of FL in healthcare.

Overall, protecting data privacy is a significant difficulty in FL. Overcoming
these challenges requires a mix of privacy-preserving strategies, strong
security precautions, and rigorous FL system design and implementation.
Moreover, Figure 10 shows the challenges of FL in different health sectors.

###  8.3  Communication and computation efficiency:

Communication effectiveness poses a crucial issue for FL since the data from
the participating devices must be sent to the central server for model
training securely and promptly [179]. The data must be delivered with the
least amount of delay possible to guarantee that the models are updated as
soon as possible and to safeguard the privacy of the user’s data. However, FL
also needs help with computing efficiency [180]. As a result, the model’s
performance may vary depending on how powerful the participating devices are.
Additionally, the models must be trained using the device’s meager resources,
which could result in excessive battery usage. FL methods are constantly
updated to address these issues [179]. Adopting model compression techniques
to shrink the model’s size may facilitate communication more quickly, which is
one way to get around the problem of communication and computation efficiency
[181]. Additionally, data partitioning, selective participation, and model
parallelism can be used to compute efficiency. In conclusion, FL faces
substantial communication and computation efficiency issues that must be
overcome to increase the technique’s overall effectiveness. Researchers and
practitioners constantly look for ways to improve communication and
computation efficiency to keep FL a viable choice for ML.

###  8.4 Handling Non-IID (Independent and Identically Distributed) data:

Non-IID (Independent and Identically Distributed) data handling is one of the
greatest issues with FL [182]. Traditionally, it has been assumed that the
training data in ML is independent and uniformly distributed (IID). However,
with FL, the information is gathered from numerous sources, some of which need
to be more independent or evenly distributed. The data’s non-IID character
presents several difficulties in FL [153, 183]. For instance, data
distribution may vary significantly between different customers, making it
difficult to train a general model that can perform well across all clients.
Non-IID data could introduce bias into training, leading to poor performance
and negative results [133].

Several methods, including client weighing, data augmentation, and transfer
learning, have been suggested to overcome these difficulties. Client weighting
involves assigning different weights to individual clients based on the
distribution of their data. This approach allows the training process to focus
more on clients with data that is more representative, improving the model’s
overall performance. By introducing noise or making minor adjustments to the
current data, data augmentation includes creating fresh data samples that can
be used to help make the data more representative. FedProx [184], SCAFFOLD
[185], and FedNova [186] are some studies that have attempted to create
efficient FL algorithms for non-IID data. Using a pre-trained model as a
starting point for training on the non-IID data is one transfer learning
method that can help overcome the challenges posed by non-IID data (see Table
7 for a comparison of data partitioning and non-IID characteristics in FL
approaches for healthcare applications).

Table 7: Comparison of Data Partitioning and Non-IID Characteristics in FL Approaches for Healthcare Applications. Study | Data Partition |  | Number
---
of Nodes
| Non-IID
---
Characteristics
Non-IID Mitigation | Study
[1] | Random | 5-10 |  | Data from different
---
hospitals
Distillation-based | [187]
[2] | Random | 5-10 |  | Different hospitals and
---
patient demographics
None | [188]
[3] | Random | 5-10 |  | Different hospitals and
---
imaging modalities
None | [189]
[4] | Stratified | 10 |  | Different hospitals and
---
patient demographics
Multi-task learning | [189]
[5] | Stratified | 4 |  | Different hospitals and
---
patient demographics
Domain adaptation | [190]
[6] | Stratified | 10 |  | Different hospitals and
---
patient demographics
| Federated transfer
---
learning
[191]
[7] | Random | 10 |  | Different hospitals and
---
patient demographics
| Local training and
---
global aggregation
[192]
[8] | Random | 5 |  | Different hospitals and
---
patient demographics
| Multi-level federated
---
learning
[193]
[9] | Stratified | 4 |  | Different hospitals and
---
patient demographics
Differential privacy | [194]
[10] | Stratified | 4-10 |  | Different hospitals and
---
patient demographics
Meta-learning | [195]

In conclusion, one of the main difficulties in FL is processing non-IID data.
This problem has been addressed using several strategies, such as client
weighting, data augmentation, and transfer learning. More study is necessary
to create more practical ways for managing non-IID data in FL.

###  8.5 Data Bias

Data bias is the term used to describe a situation in which the data used to
train the model is inaccurate or slanted in some way [186]. This may occur
when specific servers or devices have more data than others, when some servers
have different types of data than others, or when the data is not randomly
sampled. As a result, the model might develop biases in favour of the training
data, negatively affecting how well it performed on new, untrained data. Data
bias can occur in FL in a variety of ways. For instance, if some devices have
a different distribution of data or more data than others, the model may
become biased towards those devices and perform poorly on the devices with
varying allocations of fewer data. Additionally, if the data is skewed or not
sampled randomly, the final model might not accurately reflect the entire
population. Several strategies can be utilized to solve the problem of data
bias in FL. One process is to guarantee that the data’s sample size and
population representation are random. Using data augmentation techniques to
produce additional training data representative of the entire population is an
alternative strategy. Additionally, FL algorithms can be created to account
for data bias by providing greater weight to devices with fewer data or a
different distribution or by utilizing adaptive learning algorithms that
modify the model according to the data distribution on each device.

###  8.6 Limited scalability

Limited scalability is one problem with FL in healthcare. Because healthcare
data is usually scattered among several hospitals, clinics, and other
healthcare providers, organizing the data sharing and aggregation necessary
for FL can be difficult. An additional challenge is the unpredictability of
medical data. Due to the variety of healthcare data in format, accuracy, and
completeness, FL-trained ML models may perform differently. Additionally, the
data that can be shared and utilized to train ML models may be restricted due
to legal and moral restrictions on healthcare data. Researchers are looking
into several methods to increase the FL in healthcare’s capacity for scaling
to overcome these issues. One of these is establishing standards and protocols
for data sharing and collaboration among healthcare providers. Others include
creating new algorithms to handle heterogeneous and distributed data,
developing privacy-preserving techniques to safeguard sensitive information,
and creating privacy-preserving processes [196]. To guarantee that the
advantages and hazards of FL are pretty balanced and to build trust in its
application in the healthcare industry, stakeholders must also be included,
including patients, providers, and policymakers.

###  8.7 Lack of interpretability

Understanding how a model generates its predictions or judgments is called
interpretability. Transparency, accountability, and trust are just a few
reasons why it’s crucial. Nevertheless, FL’s dispersed raw data and
centralized server that aggregates model updates make it challenging to
understand the whole model. The issue of interpretability in FL can be solved
by employing techniques like differential privacy, which adds noise to the
data to protect individual privacy while still allowing the model to be
properly trained. Using techniques like model distillation is a different
tactic. The global model is trained on top of a simpler model utilizing the
same data set. Overall, FL presents interpretability challenges, but a number
of approaches can be used to address these challenges and enable the
development of trustworthy and open models.

##  9 Possible Future Research Directions

Further research in these areas can help unlock the full potential of FL for
improving healthcare outcomes while protecting patient privacy. There are
several potential research directions for FL in healthcare, including:

  * •

Developing privacy-preserving techniques: Developing privacy-preserving
techniques will be a critical focus of future research in FL in healthcare, as
patient data is susceptible and must be protected.

  * •

Model generalization: ML models developed using FL may be prone to
overfitting, which can limit their effectiveness in real-world settings.
Improving model generalization in FL can help to ensure that these models are
accurate and effective when deployed in healthcare settings. So, to enhance
the effectiveness of FL models in real-world healthcare settings, researchers
will need to focus on improving model generalization and reducing overfitting.

  * •

Identifying and addressing data biases: Biases in healthcare data can
perpetuate and amplify existing disparities and inequities. Identifying and
addressing data biases in FL ensures that these models are fair and equitable
and can help reduce healthcare disparities. So, addressing data biases can be
a focused area of future research in FL in health care in the future.

  * •

Developing new architectures for healthcare-specific tasks: Healthcare has
unique data and task requirements that may require specialized architectures
for FL models. So new architectures and models need to be developed for better
performance.

  * •

Combining FL with other technologies: FL can be combined with other
technologies, such as blockchain and edge computing, to create new healthcare
solutions. Combining FL with future technologies can help address some unique
challenges and opportunities in healthcare.

  * •

Applying FL in low-resource settings: FL can be a potent tool in low-resource
settings, where there may be limited access to data and computing resources.
It also can be an important area of future research in FL in healthcare, as it
can potentially improve healthcare outcomes in underserved areas.

Overall, there are many exciting opportunities for future research in FL in
healthcare. As the technology continues to evolve, it will be necessary for
researchers to address these challenges and develop new techniques and tools
to enable the widespread adoption of FL in healthcare.

##  10 Conclusion

In this study, we explore the application of Federated Learning (FL) in
healthcare, highlighting its potential to enhance privacy and security in
handling sensitive medical data. Our findings show that FL can significantly
mitigate risks such as data leakage and model inversion attacks through
techniques like differential privacy and secure aggregation. However,
challenges remain, particularly in managing data and device heterogeneity,
ensuring communication and computation efficiency, and addressing the non-IID
nature of healthcare data. These challenges complicate the deployment of FL
and pose significant technical, ethical, and logistical barriers to its
broader adoption. Therefore, Future work should focus on developing robust,
scalable algorithms that can efficiently handle heterogeneous and distributed
datasets while maintaining patient privacy and data security. Research should
also focus on the interpretability and fairness of FL models, ensuring they
are accessible and equitable across diverse patient demographics. Addressing
these issues will improve the operational efficiency of FL and build trust
among stakeholders, encouraging its integration into mainstream healthcare
practices. The potential impact of successfully integrating FL in healthcare
is profound, promising to revolutionize patient care by enabling more
personalized and preventive medicine while securing patient privacy.

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