factory machines) perform local data processing and transmit their learned
parameters to a cloud server for model aggregation after each time interval. A
testbed is implemented on a MNIST dataset combined with data traces from a
Raspberry Pi to simulate the network delays and resource usage when
integrating FL in IoT networks. The implementation results verify the
efficiency of FL in terms of low communication latency due to model learning
without sharing raw data and privacy preservation with guaranteed training
accuracy. Several other works also investigate the practicality of FL in real-
world healthcare settings. For instance, a federated edge learning system
called FEEL is designed in [185] for mobile healthcare. Here, an edge-based
training task offloading strategy is proposed to improve the training
efficiency at distributed health users, while a differential privacy scheme is
integrated to strengthen the privacy preservation during the FL training. A
real-world healthcare FL experiment is tested in a network of 100 hospitals
with using clump thickness as physiological attributes for building training
samples, showing a low resource consumption and good privacy protection.

In summary, we list FL-IoT applications in the taxonomy Table V and Table VI
to summarize the technical aspects as well as the key contributions and
limitations of each reference work.

TABLE V: Taxonomy of FL-IoT applications. Issue |  Ref. |  Use case |  FL type |  ML type |  FL clients |  FL aggregator |  Key contributions |  Limitations
---|---|---|---|---|---|---|---|---
FL for Smart healthcare |  [110] |  EHRs management |  HFL |  DNN |  Hospitals |  Cloud server |  A collaborative learning with FL for EHRs processing. |  Convergence of the FL algorithm has not been verified.
|  [111] |  EHRs management |  HFL |  NN |  Hospitals |  Data server |  An FL scheme for EHRs learning in distributed hospitals. |  The proposed model is simple and lacks detailed evaluations.
|  [112] |  EHRs management |  DFL |  NN |  Hospitals |  Hospitals |  A fully decentralized EHRs learning with FL. |  Privacy of EHRs has not been considered.
|  [114] |  EHRs management |  DFL |  SVM |  Health users |  Data center |  An FL-based distributed binary supervised classification scheme. |  Simulation results for FL implementation have not been reported.
|  [115] |  EHRs management |  DFL |  SVM |  Medical sites |  Data center |  An FL-based scheme for drug reaction prediction on distributed EHRs. |  The healthcare scalability of the proposed method has not been considered.
|  [116] |  EHRs management |  VFL |  - |  EHRs owners |  Data center |  A secure FLempowered scheme for EHRs computation. |  Accuracy of the FL algorithm has not been verified.
|  [118] |  Healthcare cooperation |  HFL |  NN |  Healthcare IoT devices |  Data server |  An efficient FLbased model for healthcare. |  IoT user privacy has not ben taken into consideration.
|  [119] |  Healthcare cooperation |  HFL |  CNN |  IoT devices |  Cloud server |  A cloud-edgebased healthcare model with FL. |  Attack analysis for FL has not been provided.
|  [120] |  Healthcare cooperation |  FTL |  CNN |  Hospitals |  Cloud server |  A scheme for federated activity recognition with FL. |  The scalability with more hospitals has not been investigated.
|  [122] |  Healthcare cooperation |  CFL |  RL |  Smartphones |  Data server |  A mobile FL scheme for cooperative healthcare. |  The proposed model is simple with a lack of detailed analysis.
|  [125] |  Healthcare cooperation |  DFL |  - |  Data workers |  Data workers |  A DFL architecture for healthcare with blockchain. |  Performance of blockchain has not been verified.
|  [126] |  Healthcare cooperation |  DFL |  CNN |  Data centers |  Data centers |  A P2P network for for healthcare with FL. |  FL convergence has not been investigated.
|  [128] |  Healthcare cooperation |  DFL |  CNN |  Hospitals |  Hospitals |  A DFL framework for COVID-19 image classification. |  The simulations of blockchain is lacked.
FL for Smart Transportation |  [131] |  Vehicular traffic planning |  HFL |  - |  Vehicles |  Cloud server |  An FL approach for federated traffic prediction. |  No detailed simulations are given.
|  [10] |  Vehicular traffic planning |  HFL |  RL |  Data owners |  Data center |  An FL-based scheme for traffic flow estimation. |  Numerical simulations for privacy is lacked.
|  [133] |  Vehicular traffic planning |  VFL |  - |  Car agents |  FL server |  An FL-based scheme for traffic collision avoidance tasks. |  Latency for FL simulations has not been investigated.
|  [26] |  Vehicular traffic planning |  VFL |  - |  UAVs |  Data server |  A contract model for FLbased traffic control. |  The feasibility of the proposed model has not been evaluated.
|  [27] |  Vehicular traffic planning |  DFL |  NN |  Vehicles |  Vehicles |  A decentralized FL approach with blockchain for vehicular networks. |  The impacts of blockchain running on FL have not been investigated.
|  [134] |  Vehicular resource management |  CFL |  - |  Vehicles |  RSU |  An FL approach for resource allocation and power control in ITS. |  Learning latency has not been tested.
|  [135] |  Vehicular resource management |  CFL |  DRL |  Vehicles |  BS |  A federated resource allocation scheme for V2X. |  Learning accuracy has not been investigated.
|  [63] |  Vehicular resource management |  DFL |  NN |  Vehicles |  RSUs |  A federated resource management for MEC-based V2V. |  Privacy simulation of FL has not been verified.
TABLE VI: Taxonomy of FL-IoT applications (continued).  Issue |  Ref. |  Use case |  FL type |  ML type |  FL clients |  FL aggregator |  Key contributions |  Limitations
---|---|---|---|---|---|---|---|---
FL for UAVs |  [141] |  UAV path control |  HFL |  NN |  UAVs |  Central unit |  An FL-based scheme for federated UAV path control. |  Training latency has not been analyzed.
|  [142] |  UAV communications |  HFL |  - |  UAVs |  UAV |  An FL-based framework for UAV power control and scheduling. |  Privacy of UAV communications has not been taken into account.
|  [143] |  UAV beamforming design |  DFL |  Extreme ML |  UAVs |  UAVs |  A federated beamforming design for UAV communications. |  UAV power control is not considered in FL design.
|  [145] |  UAV communications |  VFL |  CNN |  UAVs |  Data server |  An FL-enabled model for illumination distribution in UAV communications. |  The scalability of federated UAV network has not been explored.
|  [147] |  UAV energy management |  HFL |  - |  UAVs |  Central server |  A federated UAV scheme for aerial air quality sensing. |  UAV communication latency has not been taken into account.
|  [148] |  UAV security management |  HFL |  NN |  UAVs |  Data server |  A federated scheme for attack detection in UAV networks. |  Convergence latency of FL algorithms should be included.
FL for Smart city |  [48] |  City data management |  VFL |  DNN |  Vehicles |  BS |  A federated data learning for smart city. |  The proposed smart city model is simple.
|  [99] |  City data sensing |  DFL |  NN |  IoT devices |  IoT devices |  A federated sensing platform for smart cities. |  Convergence performance has not been verified.
|  [55] |  City video analytics |  VFL |  - |  Edge devices |  Cloud |  A federated model for video analytics in smart cities. |  Issues related to privacy of videos have not been investigated.
|  [157] |  Smart energy prediction |  HFL |  NN |  Data centers |  Cloud |  A federated energy prediction in smart cities. |  Comparison of ML techniques in FL simulation has not been performed.
|  [158] |  Energy load prediction |  HFL |  LSTM, DNN |  Smart meters |  MEC server |  A federated model for energy load prediction. |  Simulations for convergence and learning latency have not been conducted.
|  [159] |  Power management |  DFL |  NN |  Electric providers |  IoT users |  An FL-based scheme for power IoT networks. |  Learning efficiency, e.g., latency, has not been verified.
FL for Smart industry |  [162] |  Robotic management |  VFL |  CNN |  Robotics |  Cloud server |  An FL-based scheme for robotic management. |  Data loss caused by communication has not been considered.
|  [163] |  Federated cloud robotics |  HFL |  NN |  Robotics |  Cloud server |  An imitation learning scheme for federated cloud robotics. |  Data privacy and security have not been investigated.
|  [166] |  Federated robotic arms |  HFL |  RL |  Robotic arms |  Cloud server |  Federated reinforcement learning for robotic arms. |  Simulation is simple no details of FL results are reported.
|  [169] |  Industrial intelligence |  VFL |  - |  Mobile devices |  Cloud server |  A secure federated model learning for industrial intelligence. |  Learning performances of FL have not been detailed.
|  [170] |  Federated WiFi network |  HFL |  - |  Access points |  Central server |  A federated compound TCP scheme in industry 4.0 WiFi networks. |  Scalability for FL-based WiFi network has not been investigated.
|  [171] |  Secure industrial IoT |  DFL |  DNN |  IoT devices |  IoT devices |  An integrated FL-blockchain scheme for industrial IoT. |  Performance of smart contracts has not been detailed.
|  [174] |  Communication-efficient FL |  HFL |  NN |  Edge devices |  Cloud server |  A communication-efficient FL approach called CE-FedAvg for optimizing communication rounds. |  The impact of lazy nodes on local training has not been considered.
|  [177] |  Communication-efficient FL |  HFL |  - |  Edge devices |  Model aggregator |  A solution for reducing communication overhead for FL-based edge IoT networks. |  Resource optimization for local training has not been investigated.
|  [178] |  Network resource allocation |  HFL |  - |  Edge devices |  Model aggregator |  A fair network resource allocation framework for FL-based edge networks. |  Allocation of resource for model aggregation should be included.
|  [179] |  Network resource allocation |  HFL |  - |  Mobile users |  Edge server |  A network resource allocation scheme for FL-edge computing. |  Numerical simulations for privacy is lacked.
|  [180] |  Network resource allocation |  HFL |  DRL |  Mobile users |  Model owner |  A DRL-based approach for network resource optimization in FL-edge computing. |  The proposed FL scheme is simple and not scalable to multi-edge networks.

##  V Lessons Learned

In this section, we summarize the key lessons learned from this survey, which
thus provide an overall picture on the current research of FL-IoT services and
applications.

###  V-A Lessons Learned from FL-IoT Services

Reviewing the state-of-the-art in the field, we find that FL plays an
increasingly important role in facilitating intelligent IoT services in a wide
range of applied domains, as highlighted as follows.

####  V-A1 FL Serving as an Alternative to IoT Data Sharing

FL-IoT can achieve privacy-enhanced and scalable data sharing in IoT networks
among decentralized multiple parties without the need for direct data
offloading to cloud servers or third-parties. From [51], it can be learned
that FL is an efficient approach for federated data sharing among multiple
clients (e.g., factories) in IoT tasks such as tensor mining. By using FL, the
server only collects the ciphertext data and federates them into a tensor,
while raw data are kept at the local factories, which thus protects data
privacy for the tensor mining. Particularly, FL can be combined with other
privacy techniques such as differential privacy [53] to improve the privacy of
local updates, by integrating it into gradient descent training for enabling
secure and robust FL sharing. Moreover, the security of FL-based data sharing
can be improved by combining with the blockchain technology, as shown in [54].
In this context, the information of trained parameters can be appended into
immutable blocks on the blockchain during the client-server communications.

####  V-A2 FL for the Optimization of IoT Data Offloading and Caching

Due to the high data distribution of large-scale IoT networks, FL can provide
distributed AI solutions to support intelligent data offloading and caching at
the network edge [59]. The use of FL enables distributed data offloading by
leveraging computation capabilities of IoT devices to achieve an overall
offloading target, such as offloading latency minimization [44]. Each FL
client (such as a mobile device) acts as a local optimizer to solve an
optimization problem formulated to achieve optimal offloading. Also, the
federation of mobile users helps eliminate the need for a central server of
offloaded data processing, instead of performing local training by using their
own dataset. Then, each user exchanges the local updates for computing a
global model for privacy and computing efficiency. FL can also offer
innovative data offloading in vehicular networks [63] in which vehicles and
RSUs can share a common learning model to build a cooperative learning
architecture, aiming to reduce learning costs with respect to agent selection
and data sharing in task offloading.

In addition to that, the use of FL also helps overcome the challenges faced by
traditional learning approaches in terms of high privacy concerns since data
users may not trust the third-party server and thus hesitate to offload their
private data for IoT data caching. In fact, FL is very useful to build
proactive data caching schemes in edge computing without the need for direct
access to user data [66]. Such that, mobile users can download the AI model
from a cache entity, such as an edge server, to perform local training before
sending back the computed model to the server for aggregation in an iterative
manner, for selecting the most popular files for caching. FL is also
integrated with blockchain to build secure learning schemes for content
caching in edge computing [70]. IoT devices participate in the local training
using their own datasets to learn the features of users and files, and share
the gradient updates to the edge server for popular file estimation, aiming to
improve the overall cache hit rate.

####  V-A3 FL for IoT Attack Detection

The heterogeneity of IoT applications and services has become a main target of
malicious adversaries that can attack AI/ML models integrated in IoT networks.
FL has emerged as a strong alternative to perform distributed learning for IoT
networks with the ability to detect a wide range of attacks and support
network defense solutions [75]. Enabled by the privacy-enhancing features of
FL, federated attack detection and defense solutions can be realized where
each IoT device joins to run an AI model, such as DNN, in order to retrain the
threat model to fight against adversaries [45]. The cooperation of multiple
devices accelerates the learning process and improves learning accuracy while
mitigating the risks of attack on the model learning. Moreover, FL also
facilitates the detection of compromised IoT devices in federated IoT
networks. In fact, with the sophistication of attacks and threats, it is
challenging to detect them with current centralized AI approaches that often
recognize attacks by making deviations from user behavior profiles, which
suffers from high false alarm rate and long detection delay. FL comes as a
natural solution to perform attack detection algorithms for distributed IoT
networks [82]. Each IoT can submit a detection profile obtained by local
training to a security gateway where local updates are aggregated to build a
common detection model for the IoT network. The involvement of a large number
of IoT networks with diverse features and massive datasets enhances the
learning accuracy for better attack detection efficiency. To improve the
scalability of detection of attacks, such as intrusion, line-speed and
scalable FL approaches have recently introduced [86]. Each IoT device runs a
neural network for packet classification at the line speed of nearby switches
in a scalable manner, while preserving privacy of network traces.

####  V-A4 FL for IoT Localization

FL can provide interesting solutions for enabling efficient and privacy-
enhanced localization services, aiming to solve problems such as the lack of
robustness due to the dynamics of mobile environments and privacy leakage
bottlenecks caused by the centralized data processing architecture. As
indicated in [46], FL can provide privacy for indoor localization services by
forming a decentralized indoor localization scheme using the computational
capability of distributed mobile devices without compromising sensor data. We
also find that homomorphic encryption techniques are very useful to further
improve privacy in FL training, by allowing for local update encryption which
helps prevent the central server from guessing decryption result before
aggregation. This in turn provides high security for data training while
ensuring a high location estimation accuracy. Furthermore, according to [90],
FL is able to minimize the bias of location estimation with privacy guarantees
by cooperating multiple users in the model learning using local fingerprint
data. The usefulness of FL thus opens new opportunities for emerging
localization services, such as localization in mobile indoor networks with
global positioning system, mobile target tracking and navigation.

####  V-A5  FL for IoT Mobile Crowdsensing

Traditionally, the reliance of a central server such as a single cloud to
handle all sensing data is not a scalable solution, making it hard to cope
with the massive volume of data from ubiquitous IoT devices. FL would be a
very promising tool to accelerate the learning and training for crowdsensing
models. Each client selects a learning strategy for solving its local sub-
problem to ensure desired accuracy with lowest participation costs, while the
central server builds a utility function by averaging local updates to offer
rewards to the clients [96]. Particularly, to overcome the challenges faced by
conventional FL architectures with a central aggregator, which can incur high
communication overhead and less robustness, decentralized FL has emerged as a
promising solution for large-scale crowdsensing tasks [99]. In this way, each
IoT node only needs to compute the model based on its mini-batch instead of
the whole datasets, which will reduce computation latency accordingly. Among
decentralized technologies, blockchain is a strong candidate that can be
combined with FL to decentralize the learning process in UAV-based
crowdsensing services [100]. FL allows UAVs to train local models using
sensing datasets and share updates via the blockchain ledger for server
communication and model combination in a secure and transparent manner.

####  V-A6 FL-based Techniques for Privacy and Security in IoT Services and
Networks

FL also appears as an attractive approach for enabling intelligent privacy and
security services in IoT networks. As indicated by [101], FL is useful to
enhance data privacy in IoT environments (e.g, vehicular networks). This is
enabled by the fact that participants (e.g., vehicles) are allowed to train
models locally with their own data without the need for sharing sensitive user
data, which contributes significantly to protecting data privacy. To replace
the centralized entity that is commonly used in FL systems, blockchain is an
ideal candidate to build a decentralized FL-IoT system to solve issues of
single-point failures by using consensus protocols (e.g., shared mining) to
synchronize and coordinate the FL training [103], [106]. These solutions give
insights into developing serverless learning architectures for security
improvements in FL-IoT systems.

###  V-B Lessons Learned from FL-IoT Applications

In this sub-section, we discuss the key lessons acquired from the use of FL in
various IoT applications.

####  V-B1 FL for Smart Healthcare

FL can provide a number of efficient solutions for smart healthcare and
potentially reshapes the current intelligent healthcare systems by proving AI
functions for supporting healthcare services while improving user privacy and
reducing low latency with the cooperation of multiple entities such as health
users and healthcare providers across medical institutions [47]. It can be
learned from [110] that FL can enable flexible and privacy-preserving EHRs
management in healthcare operations, by building intelligent EHRs systems with
the cooperation of multiple medical institutions and a powerful server such as
the cloud. We also find that a few fully decentralized FL approaches [112] can
provide decentralized optimization and stochastic gradient tracking by using
the cooperation of hospitals with a decentralized stochastic gradient
algorithm to accelerate the convergence rate. Moreover, based on [119],
personalized FL is necessary for mitigating data heterogeneities in
distributed health IoT networks while attaining high-quality personalized
models. FL also allows each IoT device to choose offloading of its
computationally intensive tasks to edge gateways which execute the learning
models before sending the updates to the cloud for combination. This realizes
a cooperative network of cloud, edge, and IoT devices for healthcare
applications with the help of differential privacy and homomorphic encryption
techniques for security improvement.

####  V-B2 FL for Smart Transportation

Recently, FL has been introduced to bring AI functions to the network edge to
empower smart transportation, involving a number of participants, such as
vehicles, to collaboratively train globally shared AI models without the need
for long data transmission and compromising user privacy. Several possible
applications of FL in smart transportation can be vehicular traffic planning
and vehicular resource management. In fact, FL can be used to replace
traditional centralized ML approaches in traffic prediction tasks [10, 26] by
running ML models directly at the edge devices, e.g., vehicles, based on their
datasets such as road geometry, traffic flow and weather. The use of massive
data from multiple vehicles and huge computation capability of all participant
helps provide better traffic prediction outcomes, which cannot be met by using
centralized ML techniques with less dataset and limited computation. Moreover,
by combining with blockchain, FL is useful to build decentralized traffic
planning solutions for vehicular systems [27]. In this regard, each vehicle
acts as an FL client to run an ML model and exchange computed updates together
via a blockchain ledger while verifying their corresponding rewards. Further,
FL has the potential to facilitate resource management strategies for vehicle-
to-vehicle (V2V) networks [134]. FL along with advanced AI techniques such as
DRL [136] is promising to build federated intelligent resource allocation
strategies, in order to maximize the sum capacity of vehicular users with
respect to latency and reliability conditions. Vehicles as DRL agents can
collaborate to run distributed DNN algorithms for optimal mode selection and
resource allocation, while the BS aggregates the updates offloaded from users
to build undirected graphs using channel gain information.

####  V-B3 FL for UAVs

In UAV networks, due to the high mobility and high altitude of UAVs, it is
challenging to ensure the continuous communications between UAVs and BSs with
respect to dynamic aerial environmental conditions to perform intelligent UAV
tasks. The use of centralized AI/ML approaches in such scenarios may not be an
ideal solution, especially when having to transmit a large amount of data over
the aerial links. FL can provide better learning solutions for intelligent
UAVs networks by using a cooperation of multiple UAVs without transferring raw
data to BSs and compromising data privacy [141]. The use of FL would mitigate
the data volume transferred over the aerial environment and thus reduce
communication delays and privacy concerns, while increasing training speed of
the global model due to the employment of computing resources of all UAVs.
Moreover, FL enables UAVs to collaboratively train AI models only by using
their partial illumination data [145], which potentially reduces transmission
power and improves privacy. In this way, UAVs have more flexible solutions to
adjust its serving position and user association for communication power
savings. FL also makes federated UAV management much more flexible and with
privacy awareness [147]. Each UAV acts as an FL client to monitor the air
quality index in its coverage area without revealing raw data by cooperating
UAVs with a central server which is responsible for running a light-weight
DenseMobileNet model based on haze features offloaded from distributed UAVs.
For security protection in UAV networks, FL is combined with reinforcement
learning to implement defense strategies against jamming attack [150], by
cooperating learning updates from FL models at UAVs with a Q-learning table to
determine optimal UAV paths for minimal security risks.

####  V-B4 FL for Smart City

To realize smart cities, AI/ML techniques have been widely adopted to provide
intelligence properties thanks to their ability to handle real-time big data
generated from sensors, devices, and human activities [152]. However, most
proposed AI-based smart city solutions rely on a centralized learning
architecture on a data center, such as a cloud server, which is obviously not
scalable to the rapid expansion of smart devices in smart cities. FL offers
more attractive features for enabling decentralized smart city applications
with high privacy levels and low communication delays [48]. FL is also
important for structuring data streams from ubiquitous IoT devices that act as
FL clients for performing local learning without sharing their data with
external third-parties [153]. This would reshape the current forms of smart
cities by providing new services such as smart urban communication, social
economy sharing, social activity monitoring, and interconnection of global
citizens. Moreover, we also realize that FL can enable intelligent solutions
for smart grid, a critical component of smart city ecosystems, by offering
management and energy transmission solutions in a decentralized manner with
privacy improvement [157].

####  V-B5 FL for Smart Industry

FL can also provide viable solutions to realize intelligence for industrial
smart systems with many applied domains such as robotics and Industry 4.0
without data exchange and privacy leakage. Following [162], FL is an
attractive approach to coordinate the data learning among robotics for
industrial tasks, e.g., traffic routing, without unpredictable network
transmission delays. Edge computing is also integrated with FL to realize a
collaborative learning among robotic arm devices [166]. Due to the complexity
and dynamics, each device runs a separate ML model, e.g, reinforcement
learning, for determining its own control policy, and then shares mature
policy model parameters to a cloud server for aggregation. FL also plays an
important role in supporting distributed intelligent industrial applications
in Industry 4.0 [169]. Particularly, decentralized FL in Industry 4.0 can be
realized by combining with the blockchain technology which can provide high
degrees of security with immutable ledgers and smart contracts that can
provide authentication for learning interactions in FL implementation [171].
Furthermore, to enable intelligent and privacy-enhanced edge-based industrial
IoT applications, many efficient FL solutions have been introduced to solve
communication issues by using update reconstruction techniques [174], [175] or
relevance detection during the model update [177]. Network resource management
is another critical issue in industrial FL-IoT system design that has been
solved via resource allocation [178] and stochastic training optimization
[180].

##  VI Research Challenges and Directions

As discussed in the above sections, FL demonstrates its increasingly
significant role in empowering IoT services and applications. Despite its
great potential, the extensive survey also reveals several critical research
challenges to be considered for future FL-IoT system implementation. We here
analyze several key challenges, concerning FL such as threats, performance
issues, resource management, and heterogeneity issues in IoT networks. Several
possible research directions for these challenges are also discussed.

###  VI-A Security and Privacy Issues in FL

Although FL can provide privacy protection for distributed IoT systems where
the sharing of raw IoT data is not required in the learning process, FL still
remains several security and privacy vulnerabilities from both learning
clients and server sides [186]. For example, at the client side, an adversary
can modify data features or inject an incorrect subset of data in the original
dataset to embed backdoors into the model, aiming to adjust the training
objective of local clients. This is also called as backdoor poisoning attacks
which also poison local model updates at the clients before offloading to the
server [187]. Meanwhile, the central server may contaminate the aggregated
local updates and deploy attackers to steal the training data from gradients
in a few iterations [188] because the fact that the gradients of the weights
are the inner products of the error of learning layers and features.
Consequently, the global updating can reveal extra information about personal
features of local training data which thus poses user privacy at risks. This
issue becomes more critical in the IoT networks where user information such as
user preference, home addresses must be preserved in applied intelligent
domains with FL. Without a protection mechanism, FL becomes a security and
privacy bottleneck in intelligent IoT systems and thus makes it challenging to
attract users in the collaborative data training.

Under these contexts, developing innovative solutions to cope with threat
challenges is of paramount importance for safe FL systems. Perturbation
techniques [188] such as differential privacy or dummy can be used to protect
training datasets against data breach, by constructing composition theorems
with complex mathematical solutions. As an example, differential privacy is
applied in [189] by inserting artificial noise (e.g., Gaussian noise) to the
gradients of neural network layers to preserve training data and hidden
personal information against external threats while the convergence property
is guaranteed. This solution would ensure that the server or malicious users
cannot learn much additional information of user samples from the received
messages under any auxiliary information and attack. Similarly, an anonymous
and privacy-preserving FL scheme is introduced in [190] for the mining of
industrial big data. To mitigate privacy leakage, fewer parameters are shared
between the server and each participant while having less impact on the model
accuracy. Differential privacy is then applied on shared parameters with a
Gaussian mechanism to provide strict privacy preservation on a proxy server as
the middle layer between the server and all the participants. Moreover, the
study in [191] proposes a secure aggregation scheme for safe FL systems, in
order to provide the strongest possible security with respect server-mediated,
unauthenticated network conditions. The core idea is to use a double-masking
structure that can protect clients by encrypting local updates and key sharing
among users and the server for achieving fair data verification and key
agreement during the aggregation. The further privacy protection in FL-IoT is
still an ongoing research topic, and new innovative solutions and techniques
are desired to improve privacy for FL-based IoT systems from both client
(e.g., IoT device) and communication (e.g., over the wireless server-client
links) perspectives.

###  VI-B Communication and Learning Convergence Issues of FL-IoT

Another challenge raised from FL-IoT implementation is its limited performance
in terms of communication and learning convergence. In fact, communications in
FL training in both uplinks and downlinks are highly sensitive due to the
unbalanced and non-IID data since each training data at each client is
different in size and distribution owing to different sensing environments.
Further, when the number of clients grows exponentially, direct communications
between numerous clients and a server for offloading updates become infeasible
due to the increasing workload on the network channels [93]. In addition to
that, the convergence of FL algorithms in IoT systems is not always ensured
due to the heterogeneous training capabilities of different IoT devices and
training data complexity.  Indeed, the well-known FL algorithm FedAvg makes
limited assumption that all IoT device clients join in each communication
round for FL training [192], which is often not feasible in realistic IoT
scenarios due to device connection loss or running out of battery. Moreover,
the convergence speed of current FL algorithms is constrained due to the use
of first-order gradient descent in loss function minimization [193].

Several possible solutions have been proposed to solve issues related to FL
performances. The research in [194] proposes a new efficient communication
protocol that is able to compress uplink and downlink communications while
remaining high robustness to the increased number of clients, and data
distribution. These properties can be achieved by using a combination of
sparsification, ternarization, error accumulation, and optimal Golomb encoding
techniques for uplink compression and speeding up parallel training in the
global server without compromising learning convergence. Moreover, a new
optimization algorithm is proposed in [195] for FL-based IoT networks, called
FetchSGD, that can train high-quality models for communication efficiency. At
each communication round, clients compute a gradient based on their local
data, then compress the gradient using a data structure called a Count Sketch
before sending it to the central aggregator. The aggregator maintains momentum
and error accumulation count sketches, and the weight update applied at each
round is extracted from the error accumulation sketch. In this way, the
proposed scheme reduces the amount of communication required each round, while
still conforming to the training quality requirements of the federated
setting. To improve the convergence rate of FL algorithms, a new FL design
called Momentum federated learning is presented in [196]. A momentum gradient
descent (MGD) approach is integrated at the central server to minimize the
loss function with less computation due to optimized learning parameters,
compared to traditional FL algorithms with first-order gradient descent.

###  VI-C Resource Management in FL-IoT

The concept of FL-IoT mostly relies on scalable data parallelism and on-device
training at IoT devices before the aggregation of learning parameters at a
global server. To achieve a synchronous update at the server, all IoT devices
need to devote their storage and computing resources for their data training.
Unfortunately, this requirement is not always met due to the resource
constraints of certain IoT devices with weak computation capacities [197],
which thus can cause significant delays to the synchronized parameter
aggregation at the server. Moreover, training deep learning models such as DNN
directly on IoT devices may not be achieved due to the requirement of much CPU
frequency and battery for solving training tasks, especially training with
image and audio data [198]. Compared to edge devices, resources of IoT devices
are still very limited for large AI training. Therefore, optimizing one-device
AI/ML models would be a viable solution that can mitigate the computational
burden posed on IoT devices.

To facilitate resource management in on-device FL training, several approaches
have been provided. The work in [199] proposes a resource-aware FL
architecture on mobile devices which are used to train neural networks by
taking information of computation resource into consideration. To be clear, a
soft training technique is introduce for accelerating the training of
stragglers with weak computational capabilities. This can be done by allowing
them to train partially the model through masking a particular number of
resource-intensive neurons during their local training stage, but being
recovered in the parameter aggregation stage without compromising the overall
model convergence. For optimized on-device AI models, an improved DNN
architecture called DeepRebirth is designed in [200] for mobile AI training.
Particularly, a streamlined slimming model is integrated with the consecutive
non-tensor and tensor layer which has the potential to speed up the training
and model execution and thus can mitigate running time and save memory
resources. Simulations also confirm a high training performance with 3x
training speed and 2.5x memory saving while only 0.4% performance drop on
top-5 accuracy is observed via the ImageNet validation dataset. Another
possible solution for resource management in FL is [201] where data-importance
and compute communication-aware resource management algorithms are proposed to
optimize training accuracy, fairness and convergence time. The attention is
focused on the resource management problem of sampling clients in each round
taking into account communication, compute and statistical heterogeneity with
the goal of reducing convergence time. To be clear, importance sampling and
rank ordering based algorithms were developed that allow prioritization of
clients based on their resources in the presence of non-I.I.D data across
clients. Performance evaluation on a supervised image classification task on
benchmark datasets showed significant reduction in the overall training time
without loss of performance in the model training.

###  VI-D Feasibility of Deploying AI Learning Functions on IoT Sensors

Another possible challenge is the feasibility of running AI functions on IoT
sensors. Due to the constraints of hardware, memory, and power resources,
certain IoT sensors cannot join to train a full-size AI model [202]. In fact,
advanced ML algorithms often takes a large amount of memory and power for
model training and the storage of model parameters and training variables. For
example, training relatively simple image classification models such as
ResNet-50 still requires a number of CPU cycles and megabytes of memory space
[203]. Moreover, the high communication cost caused by AI training is also a
crucial bottleneck of on-device FL implementation. Indeed, the model exchange
between IoT sensors and the server incurs communication overhead which scales
up with the model size [204]. This would discourage IoT sensors from
participating in training large models for running on-device FL algorithms. In
addition to that, how to address the energy consumption issue in FL-IoT
systems, especially in the light-weight mobile devices with less battery
sources is another critical challenge. In this context, it is important to
achieve effective and efficient local training on local devices for energy
savings while the training quality should be ensured.

Several possible solutions should be considered to facilitate on-device AI
learning for FL-IoT systems. One direction is to improve AI hardware usage on
IoT sensors. The work in [205] introduces a software-based deep learning
accelerator to support AI/DL training on mobile hardware. The key idea is to
use a set of heterogeneous processors (e.g., GPUs) where each computing unit
exploits distinct computational resources for processing different inference
phases of DL models. This aims to optimize hardware usage for data training
without compromising performance accuracy, enabled by the control of two
algorithms, i.e., runtime layer compression and deep architecture
decomposition. Simulation results demonstrates its superior performance in
terms of low execution time and energy consumption in AI hardware running,
compared to cloud offloading-based approaches. This research is promising and
likely to enable developing sensor processing and mobile AI inference, which
would enable on-device FL implementation at scale. Additionally, a scheme
called Tiny-Transfer-Learning (TinyTL) is proposed in [206] for memory-
efficient on-device sensor learning. TinyTL freezes the weights while learning
only the bias modules, thus there is no need to store the intermediate
activations which reduces the memory footprint. To compensate for the capacity
loss, a memory-efficient bias module is integrated which improves the model
capacity by refining the intermediate feature maps of the feature extractor
with a small memory overhead. Through simulations on image classification
datasets, this approach can achieve the same level accuracy compared to fine-
tuning the full Inception-V3 while reducing the training memory footprint by a
factor of up to 12.9, which has the potential to allow for on-device AI
implementation, including FL settings on IoT sensor devices.  In terms of
communication cost in on-device FL training, the authors in [207] propose a
light-weight model training strategy, by using a concept of model output
exchange instead of model parameter exchange. Such that, only model outputs
are exchanged between IoT devices and the server, which potentially solves
communication latency issues caused by the increase of model size. This new
method can achieve a significant latency reduction up to 99% compared to
existing FL algorithms, for the similar training accuracy performance under
non-IID data distributions. Moreover, to solve the energy consumption issue in
FL-IoT systems, a number of advanced techniques are integrated, including
gradient sparsification, gradient quantization, weight quantization, and
dynamic batch sizes in the FL training procedure to mitigate energy costs for
5G mobile devices [208]. A trade-off analysis is also provided which gives
insights into how to balance the energy consumption for local computing for
model training and wireless communications for model exchange, aiming to boost
the overall energy efficiency.

###  VI-E Standard Specifications

The introduction of vertical FL-IoT use cases in future intelligent networks
imposes major architectural changes to current mobile networks in order to
simultaneously support a diverse variety of stringent requirements (e.g.
autonomous driving, e-healthcare, etc.) In such a context, network standards
and elements play an important role in deploying FL-IoT ecosystems at large-
scale due to the reliance of other important computing services such as
edge/cloud analytics server and edge-IoT communication protocols. Such
elements have the power to fill the gaps in the FL-IoT ecosystem to make it
sharper and technologically visible. For example, the Industry Specification
Group (ISG) of the European Telecommunications Standards Institute (ETSI) has
released an initiative called ETSI Multi-access Edge Computing (MEC) [209],
which aims to leverage seamless and open edge computing and communication
frameworks for integrating various edge computing-based applications
originating from the vendors, developers and third-party service providers.
All authentic operators can run their specific network domains at the edge of
deployed MEC. This initiative could be a significant element to the FL-IoT
systems where FL aggregation can be done by the support of computing services
offered by MEC. IoT data training can also be stored and processed off-line at
the edge nodes which will also facilitate services like video analytics,
augmented reality, data caching, and content delivery. Moreover, to enable FL-
IoT services and applications, standards for edge-IoT communication protocols
are essential. For example, OPC-UA has been applied widely for edge-IoT
scenarios [210]. This Open Platform Communication standard is designed to
support the platform independent Unified Architecture for edge-IoT services.
Another protocol is MODBUS which is the communication standard for connecting
industrial electronic equipment. MODBUS comes with a variety of protocols such
as remote terminal unit (RTU), TCP/IP, UDP, Plus, Pemex and Enron. It relies
on a mesh networking architecture and is able to communicate with the
supervisory control and data acquisition systems over the industrial,
scientific and medical radio bands. Among wireless protocols, Wi-Fi is one of
the most prevalent IoT communication protocols, allowing for connections
between IoT devices and computing servers such as edge nodes in FL-IoT
systems. Very recently, the IEEE 802.11 working group has initiated
discussions on releasing the next generation of Wi-Fi standard, referred to as
IEEE 802.11be Extremely High Throughput [211], which can meet the peak
throughput requirements set by upcoming IoT applications in the 5G/6G era.
These standards are expected to support service providers in deploying
intelligent IoT services at the network edge with FL-IoT components and
devices. In summary, the research challenges and directions are highlighted in
Table VII.

TABLE VII: Summary of research challenges and possible directions for FL-IoT.  Challenges |  Description |  Possible directions
---|---|---
Privacy and Security Issues in FL |  • At the client side, an adversary can modify data features or inject an incorrect subset of data into the original dataset to embed backdoors into the model [187]. • The central server can contaminate the aggregated local updates and deploy attackers to steal the training data from gradients in a few iterations [188]. |  • Differential privacy is applied in [189], [190] by adding artificial noise (e.g., Gaussian noise) to learning gradients to preserve training data and hidden personal information against external threats. • A secure aggregation scheme is proposed in [191] for safe FL systems, in order to provide the strongest possible security for data aggregation.
Communication and Learning Convergence Issues in FL-IoT |  • Communication in FL training is highly sensitive due to non-IID data distribution and increasing numbers of clients in distributed settings [93]. • The convergence rate of traditional FL algorithms is limited due to the issues of heterogeneous data and resources of distributed IoT devices [192]. |  • A new protocol is proposed in [194] for fast uplink and downlink communications with increasing numbers of clients and non-IID data. Also, an algorithm called FetchSGD is proposed in [195] for FL-based IoT networks that can train high-quality models for communication efficiency. • A Momentum FL scheme is designed in [196] to minimize the loss function and improve the convergence rate with less computation.
Resource Management in FL |  • Resource requirements for FL training are not always met due to weak computation capabilities at certain IoT devices [197]. • Training deep learning models directly on IoT devices may not be achieved due to limitations in CPU frequency and battery capacity [198]. |  • A resource-aware FL architecture is proposed in [199] to train neural networks with computation resource awareness. • An improved DNN architecture is designed in [200] for optimized mobile AI model training. Moreover, communication-aware resource management algorithms are proposed in [201] to optimize training accuracy, fairness and convergence time.
Deploying AI Functions on IoT Sensors |  • Due to constraints on hardware, memory, and power resources, certain IoT sensors cannot join to train a full-size AI model [202]. • The high communication cost and energy consumption caused by AI training places limits on on-device FL implementation [204]. |  • A software-based deep learning accelerator is introduced in [205] to optimize hardware usage for AI/DL training on mobile hardware. A memory-efficient on-device sensor learning solution is also introduced [206] which can achieve high on-device training accuracy with less memory overhead. • Energy-aware model training strategies are important for mitigating significant network latency [207] and optimizing energy costs in FL-based mobile devices [208].
Standard Specifications in FL-IoT |  • The introduction of vertical FL-IoT use cases in future intelligent networks imposes major architectural changes to current mobile networks to simultaneously support a diverse variety of stringent requirements. • Network standards and elements play an important role in deploying FL-IoT ecosystems at large-scale due to the reliance of other important computing services such as edge/cloud analytics server and edge-IoT communication protocols. |  • The initiative called ETSI Multi-access Edge Computing (MEC) has been released [209] which leverages seamless and open edge computing and communication frameworks for integrating various MEC-based applications from the vendors and service providers. • The IEEE 802.11 working group has initiated discussions on releasing the next generation of Wi-Fi standards, referred to as IEEE 802.11be Extremely High Throughput [211], which can meet the peak throughput requirements set by future IoT applications.

##  VII Conclusions

FL is an emerging distributed AI approach that has sparked great interest to
realize privacy-enhancing and scalable IoT services and applications. In this
article, we have explored the opportunities brought by FL to facilitate IoT
networks through a state-of-the-art survey and extensive discussions based on
the emerging studies in the field. This work is motivated by the lack of a
comprehensive survey on the use of FL in IoT. To bridge this gap, we have
first introduced the recent advances in FL and IoT and provided insights into
their integration. We have then provided an updated survey on the application
of FL in key IoT services, namely IoT data sharing, data offloading and
caching, attack detection, localization, mobile crowdsensing, and IoT privacy
and security. Subsequently, we have paid an attention to the discussion of
latest developments of the integrated FL-IoT applications in various
significant use-case domains, including smart healthcare, smart
transportation, UAVs, smart city, and smart industry. From the extensive
survey, several main lessons learned have been also summarized and analyzed.
Finally, we have identified a few key research challenges and possible
directions for further investigation. We believe that this article will
stimulate more attention in this emerging area, and encourages more research
efforts toward the full realization of FL-IoT.

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