# Federated Learning on Edge Sensing Devices: A Review

Berrenur Saylam<EMAIL_ADDRESS>Özlem Durmaz İncel
<EMAIL_ADDRESS>
###### Abstract

The ability to monitor ambient characteristics, interact with them, and derive
information about the surroundings has been made possible by the rapid
proliferation of edge sensing devices like IoT, mobile, and wearable devices
and their measuring capabilities with integrated sensors. Even though these
devices are small and have less capacity for data storage and processing, they
produce vast amounts of data. Some example application areas where sensor data
is collected and processed include healthcare, environmental (including air
quality and pollution levels), automotive, industrial, aerospace, and
agricultural applications. These enormous volumes of sensing data collected
from the edge devices are analyzed using a variety of Machine Learning (ML)
and Deep Learning (DL) approaches. However, analyzing them on the cloud or a
server presents challenges related to privacy, hardware, and connectivity
limitations. Federated Learning (FL) is emerging as a solution to these
problems while preserving privacy by jointly training a model without sharing
raw data. In this paper, we review the FL strategies from the perspective of
edge sensing devices to get over the limitations of conventional machine
learning techniques. We focus on the key FL principles, software frameworks,
and testbeds. We also explore the current sensor technologies, properties of
the sensing devices and sensing applications where FL is utilized. We conclude
with a discussion on open issues and future research directions on FL for
further studies.

###### keywords:

Federated Learning , Edge Computing , Sensing Devices , Mobile Sensing ,
Sensors

††journal: Internet of Things

,

Cutting-Edge Perspective: Our manuscript, titled “Federated Learning on Edge
Sensing Devices: A Review,” provides an up-to-date and comprehensive
exploration of federated learning (FL) within the context of edge sensing
devices. It offers a fresh perspective on this rapidly evolving field,
showcasing its relevance to the contemporary landscape of data analysis and
privacy preservation.

Addressing Critical Challenges: We address the pressing challenges associated
with traditional centralized data analysis methods, such as privacy concerns,
hardware constraints, and connectivity limitations. By highlighting the
limitations of conventional approaches, our research underscores the urgency
of adopting FL techniques to unlock the full potential of edge sensing
devices.

Wide-Ranging Application Domains: The manuscript sheds light on the broad
spectrum of application areas where edge sensing devices play a pivotal role,
including healthcare, environmental monitoring, automotive technology,
industrial processes, aerospace, and agriculture. Our work emphasizes how FL
can be a game-changer in extracting meaningful insights from sensor data in
these critical domains.

Comprehensive Review: We offer a comprehensive review of FL strategies
tailored specifically for edge-sensing devices. This review encompasses the
fundamental principles of FL, software frameworks for implementation, and
practical testbeds. It serves as a valuable resource for researchers and
practitioners seeking to navigate the complex landscape of FL in edge
computing.

Current Sensor Technologies: Our manuscript dives into the current state of
sensor technologies, providing an in-depth exploration of the properties and
capabilities of these devices. This insight is crucial for understanding the
foundations upon which FL operates and how it can be effectively integrated
into sensor-driven applications.

Future Research Directions: By identifying open issues and charting future
research directions, our work contributes to the ongoing discourse in the
field. We invite further investigation into the potential of FL in edge
sensing devices, offering a roadmap for researchers to explore uncharted
territories and address emerging challenges.

Timely and Substantial Contribution: Given the increasing importance of FL in
the era of edge computing, our manuscript offers a timely and substantial
contribution to the journal’s mission of advancing knowledge in the realms of
diagnostics, human health, well-being, and activity recognition with wearable
sensors.

Unpublished and Unbiased: We assure the journal that neither this manuscript
nor any of its contents are under consideration or published in another
journal, demonstrating our commitment to presenting original and unbiased
research.

##  1 Introduction

Sensor-integrated devices, such as IoT devices, wearables, smartphones,
drones, and robots, make it possible to collect vast amounts of sensing data
to learn more about or extract knowledge from the environment and the
surrounding users. Such edge devices can have varied resources regarding
memory, battery capacity, and computing power. Even though some devices may
appear more potent regarding computing capabilities, they are still considered
edge devices because extra resources might not be provided Dey et al. (2013).

Many use cases and application areas utilize the sensing data from these edge
devices. For example, GPS data from a phone can be used to determine how
congested the local traffic is, or motion-sensor data from a wearable can be
used to determine a person’s daily activity patterns Atzori et al. (2010).
Mainly, machine learning techniques are commonly applied to sensing data. The
total amount of collected data has increased as these devices are used by
masses and connected via wireless interfaces, making it easy to transfer data
for further processing. In traditional learning methods, data is usually
gathered in one location on a server-type machine, and then learning
algorithms are trained and applied to the aggregated data.

A typical machine learning pipeline involves gathering data from various
sensors to extract a significant result following an objective. However,
several challenges arise for sensor data collection and processing with a
variety of devices. Since different devices have different hardware, even
sensor data acquisition may differ. There may also be differences in the
volume of data gathered, the data sampling frequencies, the battery capacity
of the devices, and the cost of communication used to transfer the data to the
central data processing unit.

It is also challenging to get meaningful results without sufficient, high-
quality data, which is often the case with sensor data. For example, each
device may not capture the same data distribution due to its placement or
sensing trajectory. For instance, the human activity recognition field
corresponds to classifying performed activity types. Since various people may
have different characteristics, these types may vary amongst individuals. As a
result, learning algorithms’ outcomes, mainly supervised ones, may be skewed
because the training data does not contain a diverse set of classes. This
requirement forces building a model with numerous classes, preferably with a
significant amount of data for each class.

Besides, analyzing the gathered data at one location brings another challenge:
typically, this raises a privacy concern. The GDPR (General Data Protection
Regulation Voigt and Von dem Bussche (2017)) is a recently-introduced example
law to secure users’ data. Additionally, the collection of data at one
location increases the expense of communication. Furthermore, applying
algorithms as a global model may not be effective from a security perspective
since the whole system can be corrupted with one successful cyber attack,
especially for medical domains. Therefore, gathering a large volume of data in
one location is not practical.

An alternative to centralized sensor data processing is to run the machine
learning models directly on edge devices. However, edge devices have hardware
limitations that reduce their ability to execute complex learning algorithms,
particularly deep architectures. Deep learning algorithms require higher
processing power and energy in addition to memory. Consequently, we require
new mechanisms to adapt the centralized and on-device models to modest
computation restrictions Capra et al. (2019). Considering that resource
efficiency and privacy are the two critical factors in many sensing
applications for edge devices, combining data from many sources while
maintaining data privacy is necessary to produce resource-efficient learning
systems with such devices.

A promising paradigm for cooperative and privacy-preserving machine learning
in dispersed contexts is federated learning (FL) Yang et al. (2019). FL has
attracted considerable attention in several disciplines since it allows
training on local data while keeping it private and decentralized. Google
presented FL in 2016 as a solution to these problems while protecting user
privacy. The goal is to jointly train a model on each device without sharing
raw data and produce meaningful results by only sharing model parameters with
a central orchestrating unit Yu et al. (2017).

This paper aims to explore Federated Learning, a novel approach to learning to
provide a state-of-the-art assessment from the viewpoint of sensor-based edge
devices and provide an overview of FL applications on sensing devices. In
literature, some survey studies Nguyen et al. (2021); Aledhari et al. (2020)
focus on federated learning with IoT devices. In this article, we examine FL
from a broader perspective including sensing edge devices, such as IoT
sensors, wearables, and other mobile devices. Most current surveys Nguyen et
al. (2021); Xu et al. (2021); Wu et al. (2020); Sannara et al. (2021) on FL
offer overviews of one specific application case without providing the overall
state in the literature.

Sensors are measuring devices used in various applications, including medical,
environmental, automotive, industrial, aerospace, and agriculture. They detect
changes in their environment, enabling precise monitoring, control, and
decision-making processes. Medical sensors like ECGs and pulse oximeters help
diagnose heart conditions, while environmental sensors assess water quality,
pH, and dissolved oxygen. Automotive applications use light sensors, rain
sensors, flow sensors, vibration sensors, and gyroscopes for stability and
navigation accuracy. The diverse range of sensors used in various domains
contributes to producing multimodal sensor data.

In this paper, we revisit this hot topic from the standpoint of boosting
sensing devices, considering the practice of many sensing domains. Our
contribution is divided into two parts: a comprehensive review of the state of
the art on FL, including major sensing application domains, and state-of-the-
art frameworks along with testbeds for the application of FL, which are not
generally included in other surveys. Furthermore, we provide extensive
discussions that are prominent for further studies.

The structure of this paper is shown in Figure 1. In Section 2, we describe
the FL concept and working procedure, along with its primary challenges,
methods, and measurements. We introduce the most cutting-edge platforms for
using FL algorithms and a collection of testbeds to provide applicability and
encourage future studies in Section 3. Section 4 describes the different kinds
of sensors and devices and their properties. In Section 5, we put together the
application areas and current usage of FL techniques in real-life. We discuss
the state-of-the-art approaches for sensing devices, suggest further
directions in Section 6, and conclude with Section 7.

Figure 1: Schematic of the review

##  2 Federated Learning: Collaborative and Privacy-Preserving Model Training

This section delves into the field of FL, a method created to produce accurate
models while protecting the privacy of people and institutions. As mentioned,
it proposes a collaborative learning paradigm in which numerous devices take
part in training a shared model under the direction of a central server. This
section investigates the nuances of this collaboration procedure and its
various architectural strategies.

Collaborative Model Training: FL is a notion that respects privacy. Firstly
the server distributes a model with randomly initialized parameters to the
participating devices. Each device uses its local data to train the shared
model, ensuring no raw data leaves the device. Instead, the server receives
only the modified model parameters. The model averaging method is often used
to reach a stopping point in this iterative procedure Wang and Preininger
(2019); Rieke et al. (2020) (a visual scheme of the process can be seen in
Figure 2). By aggregating the updated parameters from all devices, a global
model is obtained, embodying the collective knowledge of the participants
while preserving data privacy.

###  2.1 Architectural Categories

FL encompasses three main architectural categories: horizontal, vertical, and
transfer federated learning (FTL) Yang et al. (2019). These categories are
determined based on the data distribution and the collaboration’s nature. In
the horizontal architecture, data features are similar across devices, with
variations primarily in data volume. On the other hand, the vertical
architecture focuses on data with similar data correspondence (IDs) but
differing feature spaces. Horizontal architecture, often called sample-based
FL, is commonly employed in medical cases where different local devices share
similar data structures. In contrast, vertical architecture, known as feature-
based FL, is well-suited for aggregating user data from different servers.
This architecture enables communication and collaboration between devices,
requiring aggregating diverse features Aledhari et al. (2020). An example of
horizontal architecture is the Google keyboard, where users from different
regions update model parameters locally, which are then aggregated by the
server. In contrast, the vertical architecture finds its relevance in
scenarios where multiple institutions, such as banks, collaborate to generate
personalized models by aggregating knowledge from different domains of the
same person. FTL also leverages multiple data sources to train a model on the
server. The goal is to identify commonalities across features, reducing the
overall error. FTL supports both homogeneous (different samples) and
heterogeneous (different features) training approaches, making it particularly
valuable in healthcare applications Jing et al. (2019).

Each FL architecture offers distinct advantages. The horizontal architecture
ensures data independence, allowing devices to update model parameters without
extensive communication. Vertical architecture enables collaboration between
different servers, facilitating the aggregation of diverse features. FTL
leverages different data sources to identify common patterns, ultimately
improving model accuracy Aledhari et al. (2020); Yao et al. (2021).

These architectural approaches find applications across various domains where
privacy and data security are paramount. For example, in the healthcare
domain, by leveraging FL, healthcare institutions can collaboratively train
models while preserving patient privacy, facilitating personalized healthcare
services, and advancing medical research.

Figure 2: Typical federated learning workflow Wang and Preininger (2019)

###  2.2 Challenges

Federated Learning encompasses various research areas Kairouz et al. (2021);
Saeed et al. (2020), including optimization Li et al. (2020), communication
efficiency Konečnỳ et al. (2016), personalization Wang et al. (2019), fault
tolerance Bonawitz et al. (2019), privacy preservation Bonawitz et al. (2017),
and computational efficiency Zhou et al. (2020). Likewise the case in many
methodologies, FL also has pros and downsides. These challenges mainly arise
due to device, data, and model heterogeneity (Figure 3).

Device heterogeneity: The use of different devices in data collection, such as
urban sensors, smartphones, wearable devices, and autonomous vehicle sensors
Jiang et al. (2020); Nguyen et al. (2021), introduces variations in sensing
capabilities, memory, data processing, battery life, and communication
capacity. Consequently, challenges arise, including fault tolerance,
communication efficiency, and computational efficiency, due to the diverse
nature of IoT devices.

Data heterogeneity: Device heterogeneity leads to non-IID (non-identically and
independently distributed) data, manifesting in different labels, feature
distributions, and concept drift Xu et al. (2021). For example, when data is
collected from multiple devices, each device may have only a subset of the
activity classes in Human Activity Recognition (HAR) research. Moreover,
different individuals may assign different labels to the same activity.
Furthermore, variations in feature distributions can occur due to individuals’
unique characteristics and behaviours. Addressing these challenges requires
distributed optimization techniques and personalized approaches.

Model heterogeneity: While the standard model used in basic FL architecture
facilitates aggregation, it may only be suitable for some scenarios. Model
heterogeneity arises from the diverse needs of client devices. For example,
when constructing a model for predicting user activities using data from
smartphones and smartwatches, adapting the model based on each device’s
capacity may be necessary. Additionally, clients may have privacy concerns
that discourage sharing certain model parts. This challenge underscores the
need for further research on privacy preservation and client participation.

Figure 3: Federated learning with heterogeneous devices, data collection types
and models

###  2.3 Federation Algorithms

FL algorithms vary based on the model aggregation methods used in the
literature. The initial algorithm proposed by Google, known as FedAvg McMahan
et al. (2017), employs the average function for aggregation. Subsequently,
several variations of averaging methods emerged, including weighted averaging
Shlezinger et al. (2020), one model selection, best model averaging Yao et al.
(2021), stochastic controlled average Karimireddy et al. (2020), and periodic
averaging combined with quantization Reisizadeh et al. (2020). However, FedAvg
has limitations when working with non-IID data and fails to capture fine-
grained information specific to each device Wu et al. (2020).

Other widely used algorithms include FedMA Wang et al. (2020), which
constructs a global model layer-wise using matching and averaging,
specifically designed for neural network architectures to address data
heterogeneity. FedPer Sannara et al. (2021) tackles data heterogeneity caused
by non-IID distributions by providing personalized models composed of base and
personalized layers. The base layers are aggregated on the server, while
clients focus on representation learning in the personalized layer using
transfer learning.

Moreover, the server must receive models from all participating devices to
apply any aggregation method which causes latency problems. Slower clients are
called stragglers, and the problem is called the straggler problem Zaharia et
al. (2008). In other terms, we can define this problem as a synchronous and
asynchronous learning problem. The literature proposes asynchronous Xie et al.
(2019) and semi-synchronous Wu et al. (2020) methods to overcome the straggler
problem. In addition to latency, when the server faces unbalanced and non-IID
data, even these proposed methods converge slowly Duan et al. (2019).

In Liu et al. (2021), the authors propose a partial aggregation strategy
(FedPA) where the server updates its model using models from only a subset of
clients determined by a reinforcement learning model. This parameter’s
adaptive and dynamic calculation considers data distribution and device
heterogeneity, preventing accuracy loss caused by selecting an inappropriate
aggregation number.

Similarly, in Gao et al. (2021), the authors propose the n-soft sync
aggregation algorithm to address cloud idle time. This algorithm combines the
benefits of synchronous and asynchronous aggregation by uploading only the
models of n clients in each round, striking a balance between transmission
overhead and training time. This approach ensures faster completion of
training by corresponding clients.

In Ergün et al. (2022), authors introduce FL with Secure Aggregation
(FedSecAgg), an multi-party computation (MPC)-based aggregation protocol that
ensures privacy and security in FL scenarios. It addresses the challenge of
privacy leakage during the aggregation process by employing secure MPC
techniques. This approach allows the aggregation to be performed while
preserving the privacy of individual client updates.

In He et al. (2020), Federated Meta-Learning with Model Agnostic Meta-Learning
(FedMeta-MAML) is proposed. FedMeta-MAML combines the principles of FL with
model-agnostic meta-learning (MAML). Meta-learning enables rapid adaptation to
new tasks and clients by learning a good initialization for client models.
FedMeta-MAML aggregates client updates using a meta-update rule, allowing
clients to learn how to learn from their local data.

FL with Momentum SGD (FedMoSGD) extends federated learning by incorporating
the momentum technique Xu et al. (2021), which is commonly used in stochastic
gradient descent (SGD) optimization. The momentum technique accelerates
convergence and improves the robustness of the optimization process by adding
a fraction of the previous update direction to the current update direction.

Another proposed method in literature is FedProx which adresses the challenges
of heterogeneity Li et al. (2020). It is a generalization and re-
parametrization of the popular FedAvg method, which allows for local updating
and low participation to tackle communication costs. The proposed framework
introduces modifications that address both system and statistical
heterogeneity, providing convergence guarantees in scenarios with non-
identically distributed data across devices. Empirically, FedProx demonstrates
more robust convergence and improved accuracy compared to FedAvg on realistic
federated datasets, especially in highly heterogeneous settings.

The field of FL has witnessed advancements in aggregation methods to overcome
challenges posed by non-IID data, latency, and privacy. These state-of-the-art
algorithms provide valuable contributions toward enhancing FL processes’
efficiency, privacy, and convergence.

###  2.4 Metrics

In order to evaluate and compare Federated Learning (FL) algorithms, various
metrics are employed in the literature. These metrics provide insights into
different aspects of FL performance and effectiveness. When assessing FL
algorithms, it is crucial to consider metrics that capture model performance,
communication cost, computational power, and fairness. In literature Semwal et
al. (2020), accuracy and loss for a model performance measure, number of
global rounds and the amount of transmitted data for communication cost,
number of local rounds and convergence for computational power measure,
fairness (similar performance over clients) are used as metrics.

Model performance in FL is evaluated using accuracy and loss metrics, which
measure the accuracy of predictions and the deviation between predicted and
actual values. Higher accuracy and lower loss values indicate better model
performance in terms of prediction quality and generalization. These metrics
are also used to compare FL performance with traditional ML algorithms.

Communication cost is crucial in FL systems, with metrics like global rounds
and transmitted data assessing efficiency. Minimizing the number of rounds and
reducing the transmitted data volume can optimize the overall communication
cost in FL systems.

Computational power measures evaluate algorithms’ efficiency in reaching
stable solutions. The number of local rounds reflects the computational burden
on individual client devices during training. Convergence, another important
metric, evaluates the algorithm’s efficiency in reaching a stable and optimal
solution. Faster convergence indicates a more computationally efficient
approach.

Fairness is an essential consideration in FL, aiming to ensure equitable
performance and benefits for all participating clients. Fairness metrics
assess the consistency of model performance across different clients, avoiding
biases or imbalances during the learning process.

##  3 State of the Art Federated Learning Software Platforms and Testbeds

In this section, we introduce some prominent software frameworks for building
FL applications. In addition, we provide a list of popular testbeds crucial
for the model deployment and evaluation on real devices.

###  3.1 Software Platforms

Frameworks provide developers and researchers with powerful tools and
resources to implement FL algorithms and conduct experiments in a distributed
and collaborative manner Kholod et al. (2020). We review the commonly used FL
software platforms in the literature:

  * 1.

TensorFlow (TFF (2023)): TensorFlow Federated (TFF) is developed by Google
specifically for applying deep learning over distributed data, with a
particular focus on mobile keyboard prediction and on-device learning. TFF
consists of two API layers: Core and FL API. The Core API offers default
implementations of FL algorithms such as FedAvg and FedSgd, while the FL API
allows for the definition of custom federated algorithms. TFF currently
supports simulation mode which is a controlled environment rather than being
executed on real distributed devices or edge nodes. It allows researchers and
developers to fine-tune the algorithms and observe their behavior under
various conditions without the complexity and overhead of dealing with real
distributed systems.

  * 2.

LEAF (Caldas et al. (2018)): LEAF is an open-source modular benchmarking
framework. It comprises three components: datasets, evaluation frameworks, and
reference implementations. LEAF supports various learning paradigms, including
federated, meta-learning, multi-task, and on-device learning. It provides a
benchmarking environment for evaluating FL algorithms.

  * 3.

PySyft (Community (2023)): PySyft is a framework focusing on secure and
private deep learning applications compatible with popular deep learning
libraries such as PyTorch and TensorFlow. It enables static and dynamic
computations and incorporates privacy-preserving techniques into the FL
workflow. PySyft leverages PyGrid, an API for managing and deploying PySyft-
powered models to interact with edge devices.

  * 4.

Flower (Beutel et al. (2020)): Flower is a user-friendly FL framework that
provides a facility for large-scale data scenarios and is designed to support
heterogeneous FL device environments. One of its significant contributions is
the ability to run FL algorithms directly on edge devices. Flower also
facilitates conducting experiments at the algorithmic level and considers
system-related factors such as computing capability and bandwidth. It has been
successfully tested in scenarios involving millions of clients.

  * 5.

Fate (Fedai (2023)): Federated AI Technology Enabler (Fate) is developed by
WeBank and offers extensive customization options for FL algorithms. It
comprises various components, including FATEFlow, FederatedML, FATEBoard, FATE
Serving, Federated Network, KubeFATE, and FATE-Client. Fate supports
horizontal and vertical data partitions, allowing for flexibility in data
distribution. However, it currently employs a centralized implementation,
where a server coordinates all the FL processes. It can be deployed in both
simulated and federated modes.

  * 6.

OpenFL (Intel (2023)): OpenFL is an open-source project initiated by Intel. It
provides a bash-script-based framework for FL and emphasizes secure
communication between clients. OpenFL consists of two main components: the
abrogator and the collaborator. The framework allows for the customization of
various aspects, including logging mechanisms, data split methods, and
aggregation logic. However, it requires manual client-side handling.

Among the listed FL software platforms, Flower and PySyft are particularly
relevant for working with sensor data and edge devices, including IoT devices.
Flower is a user-friendly framework supporting large-scale data scenarios and
heterogeneous FL device environments. It enables running FL algorithms
directly on edge devices, making it suitable for IoT devices. Flower considers
system-related factors such as computing capability and bandwidth, making it a
valuable choice for edge devices in sensor networks. PySyft, on the other
hand, focuses on secure and private deep-learning applications. It is
compatible with popular deep-learning libraries and allows for interactions
with edge devices. With its privacy-preserving techniques, PySyft can be
applied to IoT devices, ensuring data security during the FL workflow. These
frameworks provide the necessary support and customization options to address
the challenges associated with edge computing and facilitate efficient FL on
resource-constrained devices.

###  3.2 Testbeds

Testbeds play a crucial role in deploying and evaluating FL on the target
devices. They provide open, accessible, and reliable facilities to validate
technological solutions and foster research collaboration.

One example testbed is the Fed4FIRE
project111https://www.fed4fire.eu/news/innovation-through-the-fed4fire-
federated-testebds/, which aims to develop a common federation framework
integrating EU and US testbeds. By enabling experiments across different
domains, the project facilitates the assessment of Software Defined Networks
(SDN), explores edge computing use cases, and highlights the benefits of
interconnecting these testbeds.

In the realm of FL algorithms, the PTB-FLA Python Testbed Popovic et al.
(2023) offers a Python-based framework specifically designed for smart IoT
devices in edge systems. It supports centralized and decentralized algorithms,
providing advantages such as a small application footprint and simple
installation with no external dependencies. While the current version has
limitations, such as a maximum number of nodes and edges and a lack of
networking support, PTB-FLA shows promise for developing FL algorithms
targeting IoT devices, and future implementations aim to expand its
capabilities. This testbed includes swarms of simply IoT devices (no
computers) on its roadmap that may utilize MicroPython as an OS, which is
becoming more prevalent in embedded systems.

The FeatureCloud testbed222https://featurecloud.ai/ provides a controlled
environment for developers to test their FL applications. It offers workflow
and testbed execution modes, allowing collaborative federated training and
standalone app testing. With features like simultaneous test runs, monitoring
panels, configuration settings, and result storage, developers can quickly
validate and manage their FL apps before deployment in a federated
environment.

For realistic evaluation of FL systems, the FLAME testbed Cho et al. (2022),
introduces a novel data partitioning scheme that distributes datasets among
numerous client devices while preserving the multi-device nature of FL. This
testbed generates many users, each owning a small number of labelled data
samples, enabling developers to assess FL algorithms and models in a scalable
setting. The FLAME testbed contributes to reproducibility and further research
by open-sourcing its source code and federated HAR datasets.

These testbeds provide valuable platforms for researchers and developers to
experiment, validate, and refine their technologies in edge devices and FL
domains. Among these testbeds, Fed4FIRE, PTB-FLA, FeatureCloud, and FLAME,
demonstrate support for edge devices in federated learning. Fed4FIRE aims to
integrate various testbeds, including those supporting IoT, to enable
experiments across different domains and emphasize the importance of providing
open and interoperable testing facilities for IoT researchers and innovators.
PTB-FLA specifically targets smart IoT devices in edge systems, offering a
Python-based framework with a small application footprint suitable for smart
IoTs. FeatureCloud provides a controlled environment for testing FL
applications and supports IoT devices, allowing developers to validate and
manage their apps before deployment in a federated environment. Similarly,
FLAME supports IoT devices and creates a realistic evaluation environment for
FL systems by generating users with labelledW data samples, enabling
developers to assess FL algorithms in a more realistic IoT setting. These
testbeds collectively contribute to advancing research and innovation in
federated learning on IoT devices by offering experimentation, validation, and
refinement platforms.

##  4 Sensors and Edge-Sensing Devices

In this study, we focus on edge sensing devices such as IoT sensors, mobile
sensing devices (smartphones), wearables, and many other sensing devices that
are used in the application domains of sensor-based edge devices: medical,
environmental, automotive, industrial, aerospace, and agricultural
applications as seen in Figure 4. The devices possess three fundamental
abilities: context, computing, and connectivity Jaren et al. (2015). Context
refers to measuring information using sensors while extracting knowledge from
the sensed data. Connectivity enables communication with other devices or
central servers to facilitate knowledge transfer Jaren et al. (2015).

Figure 4: Domains of sensing applications covered in this study

The emergence of sensor technology has revolutionized data collection and
computation capabilities, giving rise to the concept of the Internet of Things
(IoT) Want et al. (2015). These small-sized sensors find applications in
various domains, including health monitoring (such as heart rate fluctuations
and step counts), communication and environment sensing in autonomous vehicles
(using cameras and proximity sensors) Rehman et al. (2017), as well as
marketplaces, agriculture, smart manufacturing, smart buildings, and energy
management McCann et al. (2018).

Besides dedicated IoT devices, our smartphones and wearables, such as
smartwatches, also have sensors. Wearable data analysis has gained significant
popularity over the past decade, with Human Activity Recognition being a
typical application area Feng et al. (2020); Sozinov et al. (2018); Ek et al.
(2020). Researchers have started focusing on more complex activities and
behaviours, such as smoking, emotion, and stress recognition, rather than
solely concentrating on basic activity types like walking and running.

Various sensors can be utilized in the target devices, including motion,
electrodermal activity, blood volume pulse, heart rate variability (HRV),
accelerometer, gyroscope, magnetometer, pressure, proximity, temperature, GPS,
sound, image, water quality, chemical, gas, smoke, infrared, humidity, and
optical sensors Srinivasan et al. (2019).

Sensing devices’ hardware properties significantly impact their performance
and capabilities. The central processing unit (CPU) is a critical component,
that executes instructions and calculations, and its performance is determined
by clock speed, core number, and cache memory. Comparing CPU specifications
across devices like smartwatches, Raspberry Pi, etc., reveals differences in
processing power and computational capabilities. For instance, smartwatches
have low-power CPUs suitable for basic tasks like notifications and fitness
tracking while Raspberry Pi devices, with quad-core ARM-based CPUs, offer
better capabilities than smartwatches and can handle lightweight desktop
applications and basic gaming. However, smart phones equipped with multi-core
CPUs provide significantly higher processing power, making them ideal for
demanding tasks like video editing, and machine learning, as well as running
resource-intensive software efficiently. GPUs, specialized processors, enhance
computational power and enable advanced graphical tasks in image processing,
augmented reality, and machine learning. While some smartwatches and Raspberry
Pi models may have integrated graphics capabilities as part of their SoC
(System on a Chip), they are not typically equipped with dedicated GPUs like
those found in PCs. Dedicated GPUs in PCs are specifically designed for high-
performance graphical tasks, such as gaming, video rendering, and machine
learning, providing much higher computational power for these tasks compared
to integrated graphics found in smartwatches and Raspberry Pi devices.

Memory is another crucial hardware component in all kinds of devices. Random
access memory (RAM) provides temporary storage for data that the device uses
during operation. Devices with larger RAM capacities can handle more extensive
datasets and run multiple applications simultaneously without experiencing
significant performance degradation. Sensing devices like smartwatches and
Raspberry Pi have significantly lower memory capacity than a typical PC.
Smart-watches and Raspberry Pi often have 512 MB to 1 GB RAM, while PCs
typically have 8 GB to 16 GB or more. Smart-watches and Raspberry Pi rely on
smaller internal storage (4 GB to 8 GB) or external MicroSD cards (16 GB to
128 GB) for data storage. In contrast, PCs have more extensive internal
storage options (256 GB to 1 TB or more). The limited memory specifications of
sensing devices can pose challenges when performing resource-intensive tasks
or processing large datasets. Developers must carefully optimize algorithms
and applications to work efficiently within the memory constraints of these
devices. On the other hand, PCs have ample memory and storage capabilities,
making them better suited for more computationally demanding tasks and data-
intensive applications.

It is important to note that the hardware properties can vary significantly
across different devices, models, and manufacturers. The specifications and
capabilities of the devices are constantly evolving, with newer generations
offering more powerful CPUs, GPUs, increased memory capacities, and additional
features along with various devices, including IoT devices, wearables,
smartphones, robots, drones, and many more. For instance, IoT devices
typically possess single or dual-core processors with limited memory and
storage. At the same time, wearables like smart watches are equipped with low-
power CPUs, moderate memory, and specialized health-related sensors. On the
other hand, smartphones feature multi-core processors, substantial memory,
high-resolution cameras, and expandable storage options. Robots may vary in
CPU configurations and memory capacity, relying on various sensors for
perception and navigation tasks. Drones often have specialized flight
controllers, moderate memory, and microSD card storage. Considering the
hardware properties of these devices is essential when developing applications
and algorithms to ensure optimal performance and compatibility with the target
hardware platform.

In this review, our main emphasis lies on sensors commonly found on edge
devices, such as motion sensors (accelerometer, gyroscope, and magnetic field
sensors), location sensors (GPS and wireless interfaces), pressure sensors,
thermometers, electrodermographs (EDA), electromyography (EMG),
electroencephalographs (EEG), electrocardiographs (ECG), oximeters, and
proximity sensors (e.g., Bluetooth). These sensors are frequently employed in
various sensing applications, often in combination, resulting in the
generation of multi-modal sensor data. In the medical field, sensors such as
electrocardiogram (ECG) sensors are used to measure and record the heart’s
electrical activity, aiding in diagnosing heart conditions. Pulse oximeters,
another medical sensor, monitor oxygen saturation levels in the blood. In the
environmental domain, water quality sensors are utilized to assess parameters
like pH levels and dissolved oxygen in bodies of water. Gas sensors are
employed for indoor air quality monitoring and detecting the presence of
harmful gases. Automotive applications involve light sensors that adjust
headlight and dashboard display brightness based on ambient light levels and
rain sensors that automatically activate windshield wipers in response to
rain. In industrial settings, flow sensors measure the flow rate of liquids or
gases in pipelines, facilitating flow control and monitoring. Vibration
sensors are utilized to detect vibrations in machinery, identify potential
faults, and prevent breakdowns. Finally, in the aerospace sector, gyroscopes
are used to measure angular motion, ensuring stability and navigation
accuracy. These examples demonstrate the diverse range of sensors utilized in
various domains to detect and measure environmental changes, enabling precise
monitoring, control, and decision-making processes.

##  5 Federated Learning Applications on Sensing Devices

Small sensing devices can capture location-based or person-based data related
to specific environments or behaviours. Data collected from multiple devices
can be combined according to the application’s requirements to gain a
comprehensive understanding of situations or behaviours. This section explores
the diverse application areas where FL techniques have been utilized (see
Figure 4). These areas include Human Activity Recognition (HAR), health
monitoring, affect detection, abnormality detection, and other relevant areas
where sensors on edge devices are utilized. We are inspired by the study
Nguyen et al. (2021) and extended it by adding different application areas.
Due to the page limitations, we did not include all studies covering these
application areas; instead, we provided examples of studies from these areas
in the literature. In Table 1, we present an overview of the covered studies,
which are explained in the following sections in detail.

###  5.1 Medical applications

In today’s world, computational-based techniques are widely used in the
healthcare domain, especially with image recognition, e.g., the detection of
diseases, the decision of cell types according to sizes and forms. The
benefits of using these techniques are fast and accurate diagnosis and
treatment Szegedi et al. (2019); Wang et al. (2023). However, data protection
regulations make it impossible to collect and analyze these data easily. In
this regard, FL techniques are well suited to healthcare-related applications
from the privacy perspective. In addition, intrinsically, FL is designed for a
vast number of client participants.

  * 1.

Disease Diagnosis: The study Ogbuabor et al. (2021) proposes an intelligent
and privacy-oriented context-aware decision support system for cardiac health
monitoring using physiological and activity data of the patient during
rehabilitation, utilizing a federated machine learning approach for activity
recognition while maintaining users’ privacy. The system allows healthcare
professionals to assess the health status of patients by considering multiple
relevant parameters, such as heart rate, electrocardiogram (ECG) signals, and
activity data. It uses the data collected from Holter monitors and smartphones
equipped with accelerometers. The federated machine learning approach enables
the development of a collaborative model without sharing sensitive patient
data, ensuring data privacy while achieving model generalization.

  * 2.

Affect Detection: Affective computing Picard (2000) is an active research area
for automatically monitoring a person’s mental and emotional state via
physiological and physical signals. In Can and Ersoy (2021), the authors
applied FL on stress data with the aim of privacy preservation. The authors
applied FL on heart activity data collected with smart bands, and the targeted
devices include wrist-worn wearable devices (e.g., Samsung Gear S, Empatica
E4) and smartphones, with various physiological sensors such as
accelerometers, electrodermal activity, heart rate, etc. The proposed
federated deep learning algorithm achieves encouraging results in stress-level
detection while ensuring privacy protection for health-related research
conducted with widespread mobile unobtrusive devices. Two use-case scenarios
are considered: creating a general model from individual models while
preserving user privacy and aggregating separately collected event data to
develop an improved shared model. They applied deep learning algorithm in a
federated manner and obtained better results compared to traditional learning
methods.

  * 3.

Human Activity Recognition: HAR aims to detect users’ activities, such as
sitting, standing, and walking, mostly via motion sensors. The sensors used in
HAR tasks typically include triaxial accelerometers and gyroscopes found in
smartphones and smartwatches. These sensors measure acceleration forces and
rotational movements, allowing the models to recognize various activities such
as walking, running, biking, and more. In Feng et al. (2020), FL models were
applied for trajectory prediction. The framework utilizes various sensing
devices and sensors present in mobile devices to gather location-based data.
The paper introduces a group optimization method for training on local devices
to achieve a better trade-off between performance and privacy. In Sozinov et
al. (2018), authors applied FL to detect basic human activities, where the
goal is to recognize different human activities using sensor data from
smartphones or smartwatches. They obtained good accuracies but slightly lower
than a centralized one. In Ek et al. (2020), they focused on the HAR domain
and applied several aggregation algorithms (FedAvg, FedPer, and FedMA) and
compared their results.

###  5.2 Environmental applications

FL has emerged as a powerful approach to tackle environmental challenges like
climate change, air pollution, and extreme weather events.

  * 1.

Air Pollution Monitoring: FL is employed to predict air pollution using sensor
networks in Nguyen and Zettsu (2021). The utilized sensing devices are
environmental monitoring stations with various sensors, which are not
specified in the paper. Nevertheless, a combination of sensors is typically
deployed to collect data on various environmental parameters such as
particulate matter (PM), nitrogen dioxide (NO2), ozone (O3), carbon monoxide
(CO), sulfur dioxide (SO2), meteorological data (temperature, humidity, wind
speed, etc.), and more. The paper focuses on predicting air pollution levels,
particularly Oxidant warning levels, using Convolutional Recurrent Neural
Networks (CRNN). The CRNN models are trained locally on data from different
spatial areas, such as cities and prefectures, and then the common parts of
these models are aggregated at the server to create a global model. This
federated learning framework allows for cooperative training among
participants from different regions without exchanging raw data, ensuring data
privacy and reducing data transmission latency.

  * 2.

Weather Forecasting: FL is utilized for weather prediction using distributed
weather stations Chen et al. (2023). The stated paper introduces a novel
machine learning approach, MetePFL, for weather forecasting on a global scale.
The proposed approach utilizes a foundation model (FM) pre-trained on
extensive weather data and fine-tuned by regions to capture local weather
patterns. MetePFL employs prompt federated learning to train a Transformer-
based FM collaboratively across participants with heterogeneous meteorological
data. A spatiotemporal prompt learning mechanism is introduced to handle
multivariate time series data efficiently, improving forecasting accuracy. The
experiments conducted on accurate weather forecasting datasets demonstrate the
effectiveness and superiority of the MetePFL approach over traditional fine-
tuning and other federated learning methods. By utilizing only a fraction of
the model’s parameters, MetePFL achieves excellent performance while reducing
inter-device communication overhead, making it a promising solution for
comprehensive weather forecasting on large-scale meteorological data.

###  5.3 Smart city applications

FL techniques are also applied to integrated city solutions related to food,
water, and energy So et al. (2021). Applications use data collected from
cameras, temperature sensors, and traffic sensors to understand human
activities, population density, and pollution Jiang et al. (2020).

  * 1.

Traffic Pattern Analysis: FL is employed for traffic flow prediction in smart
cities Liu et al. (2020). The paper Liu et al. (2020) presents FedGRU, a novel
privacy-preserving traffic flow prediction algorithm based on FL. The goal is
to develop a collaborative model that can accurately predict traffic patterns
and congestion levels using data from various sources, such as traffic cameras
and sensors. The proposed algorithm uses a secure parameter aggregation
mechanism. The paper introduces a joint-announcement protocol to handle large-
scale scenarios, randomly selecting a subset of organizations for each
training round, effectively mitigating communication challenges. Empirical
case studies demonstrate that FedGRU achieves comparable prediction accuracy
to centralized models while maintaining robust privacy protection. An ensemble
clustering-based approach is proposed to enhance model performance by
leveraging spatiotemporal correlations among organizations’ data.

  * 2.

Smart Transportation: Transportation is composed of two parts: vehicular
traffic planning and resource management Nguyen et al. (2021). Traffic
planning is essential for traffic prediction and congestion minimization. In
Elbir et al. (2022), authors replaced centralized ML models with FL models for
traffic prediction cases and ran the model on edge devices. As data, they
utilized traffic flow, weather, and road geometry. In Liang et al. (2022), a
traffic simulator is designed with FTL to guide Reinforcement learning agents’
(FTRL) driving. The proposed framework provides collision avoidance as well.

###  5.4 Industrial applications

Processing industrial data with AI techniques makes possible high-precision
location detection, the creation of early warning systems, and the detection
of abnormal behaviors. Thus, with smart security, pre-warnings, and post-event
analysis can be performed. The manufacturing process comprises modeling,
monitoring, prediction, and control stages Ge et al. (2017). The smart
industry is a concept about integrating ML techniques into this process.

  * 1.

Predictive Maintenance: FL is applied to develop models for predictive
maintenance in industrial settings in Pruckovskaja et al. (2023). The paper
evaluates FL aggregation methods for predictive maintenance and quality
inspection in Industry 4.0, ensuring data privacy. Four strategies, FedAvg,
FedProx, qFedAvg, and FedYogi, are explored. Data distributions significantly
impact FL performance. The authors introduce a real-world quality inspection
dataset (FLADI) with diverse product variants as clients. FL relies on
decentralized training, where clients train local models and share updates
with a central server. Targeted sensing devices include machines, production
units, sensors, and intelligent factories. FL’s suitability varies based on
data distribution and feature heterogeneity among clients.

  * 2.

Monitoring and Anomaly Detection: In Han et al. (2019); Liu et al. (2020),
authors use FL for monitoring the environment collaboratively using various
sensors to detect defects. In Zhai et al. (2021), authors developed an anomaly
detection system using FL for IoT devices equipped with an ammeter, water
meter, and camera sensors. In Mowla et al. (2019), it is used for attack
detection for Unmanned Aerial Vehicles, which can be considered anomaly
detection.

  * 3.

Robotics and Industry 4.0: FL is used for allocating devices for real-time
data processing in robotics. For instance, in Zhou et al. (2018), authors used
FL to decrease the communication time, facilitating learning on the device. In
Liu et al. (2020, 2019), FL is applied for learning an imitation scheme. Each
device runs the NN model independently and shares the parameters with the
server, which increases the global knowledge knowledge leading to more
accurate results.

###  5.5 Aerospace applications

The aerospace industry is equipped with cameras (for visual data), LiDAR
(Light Detection and Ranging) sensors (for 3D mapping and obstacle detection),
GPS (Global Positioning System) receivers (for location information), and
inertial sensors (gyroscopes and accelerometers for attitude and motion
tracking), is continually seeking innovative ways to enhance the safety,
efficiency, and reliability of aircraft operations. Key to achieving these
objectives is the development of advanced technologies that enable real-time
monitoring of aircraft health and optimizing critical resources Fu et al.
(2023).

  * 1.

Aircraft Health Monitoring: FL is employed for aircraft monitoring using
distributed sensor networks in Li et al. (2022). The study addresses the
challenge of intelligent fault diagnosis in aerospace equipment, focusing on
bearings as critical components. Data-driven approaches have shown promise,
but the scarcity of labelled data and data distribution discrepancies among
different equipment hinder model training. The authors propose a novel method
using Clustering Federated Learning (CFL) with a self-attention mechanism to
overcome this. Sensors collect vibration signals from various devices, and the
CFL approach clusters clients with similar data distributions. The proposed
method achieved superior fault diagnosis results compared to other methods,
demonstrating its effectiveness in leveraging distributed data for improved
safety and maintenance of aerospace equipment.

  * 2.

Power Allocation and Scheduling: FL is applied to optimize power and schedule
in the aviation industry. The goal is to develop a shared model to improve
fuel efficiency, reduce emissions, and enhance flight safety. Airlines or
aviation companies train local models using their flight data, and the models’
updates are aggregated to create a global model. By collaboratively learning
from diverse flight patterns, FL enables optimized flight planning without
sharing sensitive flight information. The paper Zeng et al. (2020) presents a
novel framework for distributed FL in a swarm of unmanned aerial vehicles
(UAVs). Each UAV trains a local FL model based on its data, and a leading UAV
aggregates these models to generate a global one, optimizing convergence using
joint power allocation and scheduling. The study analyzes how wireless factors
impact FL performance, highlighting the effectiveness of the proposed
approach.

###  5.6 Agricultural applications

The agricultural applications exemplify how integrating smart sensors and FL
revolutionizes the state of the art. By empowering farmers with automated
disease detection, crop monitoring, and efficient resource management, these
technologies play a pivotal role in maximizing agricultural productivity and
sustaining livelihoods in the agricultural sector.

  * 1.

Crop Disease Detection: FL is utilized for collaborative crop disease
detection in Ebenezer et al. (2022). The method involves training machine
learning algorithms across a decentralized network of edge devices or servers,
each retaining its local data samples. By utilizing image datasets of crop
diseases and employing convolutional neural networks (CNNs), FL enables the
creation of a powerful deep-learning model for disease identification. This
approach eliminates the need for costly and continuous professional
inspection, making disease detection more accessible and cost-effective for
farmers, even in remote areas. FL’s privacy-preserving nature ensures farmers
can contribute their crop images without sharing raw data. Implementing FL for
crop disease detection promises to enhance agricultural productivity, ensuring
food security and economic growth for farming communities and the entire
country.

  * 2.

Agricultural Production: Another application area of FL is agricultural
production Abu-Khadrah et al. (2023). In this study, authors utilize various
smart sensors for agricultural monitoring and control. The specific sensors
used include optical sensors, mechanical soil sensors, location sensors,
electrochemical sensors, dielectric soil moisture sensors, airflow sensors,
and electronic sensors. These sensors are deployed in smart agricultural plots
to sense crucial parameters such as temperature, pressure, humidity, soil
moisture, crop health (infections and growth level), and climate conditions.
The target device for this application is the smart sensor system, which
integrates with IoT technologies and cloud computing for decentralized data
processing and global actuation. The sensor system, using FL, collaborates
with the cloud server and other sensor devices to optimize sensor controls
based on past accumulated data. By modifying the sensor operations according
to the validated and optimal data, the smart sensors enhance agricultural
production and maximize productivity in various agricultural plots.

Table 1: A summary of Sensing Applications on Edge Devices with Federated
Learning

Application | FL Algorithm | FL Methodology | Sensors | Target Edge-Sensing Devices
---|---|---|---|---
Cardiac Health Monitoring (Ogbuabor et al. (2021)) | FedAvg | Horizontal | Accelerometer | Smartphones
Biomedical Monitoring (Can and Ersoy (2021)) | FedAvg | Horizontal, FTL | PPG | Wearable IoT devices
Human Activity Recognition (Sozinov et al. (2018)) | FedAvg | Horizontal | Accelerometer, gyroscope | Wearables (Smartphones/watches)
Evaluation of FL Algorithms (Ek et al. (2020)) | FedAvg, FedPer, FedMA | Horizontal | Accelerometer, gyroscope | Smartphones
Air Pollution Prediction (Nguyen and Zettsu (2021)) | Convol. Recurrent Network, | Vertical | Temperature, humidity, wind | Environmental monitoring
| Averaging Aggregation |  | speed, atmospheric sensors | stations
Weather Forecasting (Chen et al. (2023)) | MetePFL | Horizontal | Meteorological sensors | Weather stations
Traffic Flow Prediction (Liu et al. (2020)) | FedGRU | Horizontal | Traffic cameras and sensors | Edge-sensing devices for traffic flow
Autonomous Driving (Liang et al. (2022)) | FTRL | Horizontal | LiDAR | NVIDIA Jetson TX2
Predictive Maintenance (Pruckovskaja et al. (2023)) | FedAvg, FedProx, qFedAvg, | Horizontal | Air pressure, temperature, | Edge devices in industrial settings
| FedYogi |  | speed, force |
Quality Inspection (Zhai et al. (2021)) | ”cli-max greedy” for FL | Horizontal | Ammeters, water meters, | Computing-enabled mobile nodes,
|  |  | cameras | intelligent grid terminals
Aircraft Health Monitoring (Li et al. (2022)) | Clustering FL (CFL) | Horizontal, | Vibration sensors | Aerospace’s machinery
|  | Vertical, FTL | (accelerometers) |
Flight Path Optimization (Zeng et al. (2020)) | FedAvg | Horizontal | GPS, IMU, cameras, LiDAR, | UAVs
|  |  | other environmental sensors |
Crop Disease Detection (Abu-Khadrah et al. (2023)) | FedAvg | Horizontal | Soil, location, electrochemical, | Agricultural equipment, drones,
|  |  | airflow, electronic sensors | or IoT devices

##  6 Discussion

The discussion section introduces several points that have emerged from the
literature review on applying FL methodologies to sensing devices.

Firstly, it is noteworthy that most studies in the literature predominantly
focus on synchronized FL, where training and model transfer occur in a
synchronized manner Gao et al. (2021). However, the reality of systems and
data heterogeneity often leads to asynchronous training and model transfer,
posing challenges in scaling federated optimization. To enable efficient and
scalable federated optimization, future research should explore techniques and
algorithms that effectively handle the asynchrony inherent in FL settings.

Another key point to consider is the assumed labelled data in FL when using
supervised algorithms, while on-device data is typically unlabelled Jing et
al. (2019). This presents a challenge when applying FL methodologies directly
to on-device sensing tasks. Future research should concentrate on developing
techniques that can effectively leverage the unlabelled on-device data and
explore strategies for incorporating label acquisition processes within the FL
framework.

It is worth noting that many FL studies in the literature have primarily
focused on simulation environments. While simulations provide controlled and
reproducible settings, there is a need for more studies that adapt FL
methodologies to real-world applications. Real-world deployment of FL on
sensing devices introduces additional challenges, including communication
constraints, privacy concerns, and device heterogeneity. Further research
should aim to bridge the gap between simulation and real-world deployment,
ensuring the practicality and effectiveness of FL methodologies in real-world
sensing applications.

The impact of wireless communication and resource efficiency is a significant
aspect when applying FL methodologies to sensing devices. While FL minimizes
data transmission by uploading local updates instead of raw data, the size of
the trained model parameters, especially with Deep Neural Networks (DNNs), can
still be substantial. This poses a challenge for resource-constrained,
battery-operated devices that may require numerous wireless communication
rounds and iterations.

Efficient wireless communication is crucial in distributed machine learning to
address the limitations of resource-constrained devices. Various methods can
be employed to achieve communication efficiency, such as reducing the size of
model updates, optimizing communication frequency, and employing selective
client participation. These approaches aim to minimize the data exchanged
during model updates, reducing communication overhead.

Training DNN models on resource-constrained clients, such as wearable and IoT
devices, presents a significant challenge. These devices often have limited
computation power, memory, and energy resources. The computational cost of
running the full DNN model can be prohibitively high for such devices.
Therefore, it is essential to design computationally efficient algorithms,
considering the limited processing capabilities of these devices. Optimizing
processing speed becomes crucial as it directly impacts the algorithm’s
throughput, latency, and response time.

Furthermore, personalization is a key aspect that holds potential in applying
FL methodologies to sensing devices. The ability to tailor the learning
process and model parameters according to individual device characteristics
and user preferences can significantly enhance the performance and user
experience of FL-based applications. One approach to personalization in FL is
to adapt the training process to individual sensing devices’ specific
capabilities and constraints. As mentioned, different devices possess varying
computational power, memory capacity, and energy resources. By considering
these device-specific factors during the training phase, it is possible to
optimize the learning process and model updates to suit the capabilities of
each device. This personalized training approach can improve performance and
energy efficiency by leveraging individual devices’ strengths and limitations.

Another aspect of personalization in FL is user-centric customization. Sensing
devices are often intimately connected to their users, capturing personal data
and insights. By incorporating user preferences and characteristics into the
learning process, FL can create personalized models that cater to specific
user needs. This can be particularly relevant in applications such as health
monitoring or affective computing, where individual variations and preferences
play a significant role. Personalizing the FL models based on user-specific
data can greatly enhance the accuracy and relevance of the generated insights.

Privacy is a critical concern in FL, especially when dealing with personal
data. However, personalization and privacy can coexist through the adoption of
privacy-preserving techniques. Techniques such as differential privacy,
federated encryption, and secure aggregation can be employed to protect
sensitive user data while still enabling personalized learning. By ensuring
that personalization is conducted privacy-consciously, FL can strike a balance
between customization and data protection, gaining user trust and fostering
broader adoption.

Moreover, personalization in FL can extend beyond individual devices and users
to cater to specific application domains. Different sensing applications, such
as medical, environmental, automotive, industrial, aerospace, and
agricultural, may have unique requirements and objectives. By tailoring the FL
methodologies and model architectures to the specific demands of each domain,
personalized FL solutions can be developed to address the distinct challenges
and opportunities presented by different application contexts.

##  7 Conclusion

This paper has provided an overview of the current state-of-the-art FL
methodologies applied to sensing devices, focusing on IoT sensors, mobile
devices, and wearables. By examining the challenges and opportunities in
applying FL to sensing tasks, the paper aims to inspire further research in
this domain.

The discussion section has raised several important points for consideration.
It highlighted the need to address the asynchrony in FL, leverage unlabelled
on-device data, explore FL applications in Human Activity Recognition (HAR)
and affective computing, and adapt FL methodologies from simulation
environments to real-world applications on sensing devices.

Efficient wireless communication, resource efficiency, and personalization are
crucial when applying FL methodologies to sensing devices. Future research
should focus on developing wireless communication techniques that minimize
data exchange, designing algorithms with resource constraints in mind, and
enabling privacy-preserving personalization. By addressing these challenges,
FL methodologies can be effectively applied in real-world sensing
applications, considering sensing devices’ unique constraints and
requirements.

Moreover, the discussion emphasizes the need for studies that bridge the gap
between simulation and real-world deployment, ensuring the practicality and
effectiveness of FL methodologies in real-world sensing applications. By
adapting FL methodologies to real-world settings, researchers can address
additional challenges, such as communication constraints, privacy concerns,
and device heterogeneity, and validate the performance of FL in diverse
sensing domains.

In conclusion, this paper aims to provide a comprehensive review for
researchers and practitioners interested in applying FL methodologies to edge
sensing devices to contribute to developing intelligent and efficient systems
in diverse application domains by highlighting the specific challenges and
opportunities associated with these devices.

## Acknowledgment

Tübitak Bideb 2211-A academic reward is gratefully acknowledged. This research
has been supported by the Boğaziçi University Research Fund, project number:
19301P.

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