Table 2 Applications of Federated Learning in Cloud-Edge Architecture

Lu et al. [94]

IOV

Protect passenger privacy

GCN

20newsgroups

better than VFL

Ye et al. [85]

IOV

Reduce the Impact of Heterogeneity Problems in Vehicle Clients on Federated Learning Performance

selective polymerization

MNIST & BelgiumTSC

better than approaches based on FedAvg.

Bao et al. [95]

IOV

Implement client selection and networking solutions in a car networking environment

Fuzzy logic algorithm

n/a

Communication-efficient

Boualouache and Engel [86]

IOV

Detect passive mobile attackers in 5G vehicle edge computin

MLP

n/a

Fast Detection & High Accuracy

Xu et al. [96]

IOV

Accurately schedule and dynamically reserve the appropriate amount of multimedia service resources on edge servers.

ST ResNet

n/a

Secure and efficient

Fantacci and Picano [97]

Demand prediction

Protect sensitive user data

FedAvg

MovieLens 1M & MovieLens 100K

better than the approach based on chaos theory and deep learning.

Taïk and Cherkaoui [98]

Household load forecasting

Protect user privacy

FedAvg

n/a

significant gain in the network load

Rahbari et al. [99]

UAV

Improve resource utilization in real-time applications.

Aggregate by scoring weight

n/a

better fairness & energy efficient

Pham et al. [100]

UAV

Improve the transmit power efficiency of UAVs

Decomposition

n/a

Dramatically reduce drone launch power

Chen et al. [101]

Augmented Reality

Improve computational efficiency & Reduce latency

CNN

CIFAR-10

Fewer training iterations

Hsu et al. [102]

Information Security

Android malware detection

SVM

from NICT

Outperforms centralized training systems.

Wang et al. [103]

Industry

Industrial Equipment Troubleshooting

Asynchronous update

n/a

Communication-efficient & Fast Convergence

Zhang et al. [104]

IOMT

Adapt FL to train AHD models

FedSens

real-world AHD applications

strong for biased class distributions

Qayyum et al. [89]

Healthcare

Automated diagnosis of COVID-19

CFL

COVID-19 CT segmentation

Outperforms traditional FL models

Yuan et al. [105]

Intelligent Transport

Traffic flow forecast

FedSTN

n/a

accurate and fast prediction

Vyas et al. [106]

Intelligent Transport

Calculate driving stress and the relationship between driving stress and driving behavior

Long Short-Term Memory Fully Convolutional Network

UAH

High pressure prediction accuracy

Sada et al. [107]

Video Analysis

Distributed video analysis framework

distributed object detection

n/a

Real-time Distributed Object Detection

Chen et al. [87]

Cyber Security

Intrusion detection of wireless networks

FedAGRU

KDD CUP 99 & CICIDS2017

Communication-efficient & strong robustness against poisoning attacks

Li et al. [93]

Cyber Security

Detect network attacks

FLEAM

n/a

Approximate accuracy to centralized training. & Greatly increase detection rate.

Huong et al. [92]

Cyber Security

Quickly and accurately identify cyber attackers

Centralized and distributed methods

BoT-IoT

Accuracy and complexity outperform CNN, SVM and other algorithms.

Hu et al. [108]

Smart City

Urban environment sensing

FRL

n/a

Energy-efficient