Visible light communication (VLC) technology was introduced as a key enabler for the next generation of wireless networks, mainly thanks to its simple and low-cost implementation. However, several challenges prohibit the realization of the full potentials of VLC, namely, limited modulation bandwidth, ambient light interference, optical diffuse reflection effects, devices non-linearity, and random receiver orientation. On the contrary, centralized machine learning (ML) techniques have demonstrated a significant potential in handling different challenges relating to wireless communication systems. Specifically, it was shown that ML algorithms exhibit superior capabilities in handling complicated network tasks, such as channel equalization, estimation and modeling, resources allocation, and opportunistic spectrum access control, to name a few. Nevertheless, concerns pertaining to privacy and communication overhead when sharing raw data of the involved clients with a server constitute major bottlenecks in the implementation of centralized ML techniques. This has motivated the emergence of a new distributed ML paradigm, namely federated learning (FL), which can reduce the cost associated with transferring raw data, and preserve privacy by training ML models locally and collaboratively at the clients' side. Hence, it becomes evident that integrating FL into VLC networks can provide ubiquitous and reliable implementation of VLC systems. With this motivation, this is the first in-depth review in the literature on the application of FL in VLC networks. To that end, besides the different architectures and related characteristics of FL, we provide a thorough overview on the main design aspects of FL based VLC systems. Finally, we also highlight some potential future research directions of FL that are envisioned to substantially enhance the performance and robustness of VLC systems.
FedSPM: Routing-Enabled Federated Learning under Dual Heterogeneity via Semiparametric Mixture
Institute of Statistics and Big Data, Renmin University of China（中国人民大学统计与大数据研究院）；Department of Statistics and Actuarial Science, University of Waterloo（滑铁卢大学统计与精算科学系）
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（网络与交换技术国家重点实验室，北京邮电大学）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；The Future Network of Intelligence Institute, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）未来智联网络研究院）；School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理工学院）
Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning
School of Computing, Newcastle University（新castle大学计算机学院）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）；Institute of Health Informatics, University College London（伦敦大学学院健康信息学研究所）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（上海智能自主系统研究所，同济大学）；College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；University College London Hospital（伦敦大学学院医院）
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain
School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Chainbase Inc.（Chainbase公司）；PublicAI Inc.（PublicAI公司）
Quantum Cloud and Distributed Systems (qCLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Australia（量子云与分布式系统实验室，计算机与信息系统学院，墨尔本大学，澳大利亚）；Department of Computer Science and Engineering, University of Visvesvaraya College of Engineering, Bangalore University, India（计算机科学与工程系，维萨瓦拉亚工程学院，班加罗尔大学，印度）
Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communication
School of Electrical and Data Engineering, University of Technology Sydney（悉尼技术大学电气与数据工程学院）；Thrust of Internet of Things, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）物联网研究所）
From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks
Hung Yen University of Technology and Education（兴安技术与教育大学）；University of Tasmania（塔斯马尼亚大学）；University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；Lac Hong University（乐鸿大学）；Manchester Metropolitan University（曼彻斯特城市大学）；International Hellenic University（国际希腊大学）
Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission
Department of Computing, Gachon Univ.（高恩大学计算机系）；School of Computer Engineering, Nanyang Technological University（南洋理工大学计算机工程学院）；Department of Electrical and Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications
Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tsinghua University（清华大学）；RISC-V International Open Source Laboratory（RISC-V国际开源实验室）；National Mobile Communications Research Laboratory（移动通信国家重点实验室）；Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；George Washington University（乔治华盛顿大学）；Ryerson University（瑞尔森大学）
Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance
School of Computer Science, Hunan University of Technology and Business（湖南工商大学计算机学院）；Xiangjiang Laboratory（湘江实验室）；School of Intelligent Software and Engineering, Nanjing University（南京大学智能软件与工程学院）；Hunan Normal University（湖南师范大学）；Department of Computer Science, Brunel University London（布鲁内尔伦敦大学计算机学院）
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study
Centre of Informatics and Systems of the University of Coimbra, CISUC（科英布拉大学信息与系统中心）；University of Coimbra（科英布拉大学）；Institute for Systems Engineering and Computers, INESC（系统工程与计算机研究所）；Sul-Rio-Grandense Federal Institute (IFSUL)（南里奥格兰德联邦学院）；Instituto de Telecomunicações（电信研究所）；Polytechnic Institute of Coimbra（科英布拉理工学院）；Coimbra Business School（科英布拉商学院）；INESC Coimbra（科英布拉INESC）
Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation
Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)（深圳未来智能网络研究院）；School of Science and Engineering (SSE)（科学与工程学院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Advanced Technology (SIAT)（深圳先进技术研究院）；Chinese Academy of Sciences (CAS)（中国科学院）；Southern University of Science and Technology（南方科技大学）；Imperial College London（伦敦帝国学院）；Central South University（中南大学）；Shenzhen University of Advanced Technology（深圳理工大学）
OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework
Meituan（美团）；Bytedance Inc.（字节跳动公司）；Tsinghua University（清华大学）；University of Macau（澳门大学）；State Key Lab of Computer Science, ISCAS（中国科学院软件研究所计算机科学国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Fairness-Aware Client Selection for Federated Learning
Nanyang Technological University (NTU)（南洋理工大学）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-新加坡南洋理工大学联合研究院）；Alibaba Group（阿里巴巴集团）；University of Science and Technology of China（中国科学技术大学）
