The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end users, these data usually have high heterogeneity and privacy, while most of users are reluctant to expose them to the public view. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue, such that it has attracted extensive attention from academia and industry. As a new machine learning (ML) paradigm, federated learning (FL) has great advantages in training heterogeneous and private data. This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments. In order to increase the model aggregation rate and reduce communication costs, we apply deep reinforcement learning (DRL) to IIoT equipment selection process, specifically to select those IIoT equipment nodes with accurate models. Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment. By analyzing the data characteristics of IIoT equipments, we use MNIST, fashion MNIST and CIFAR-10 data sets to represent the data generated by IIoT. During the experiment, we employ the deep neural network (DNN) model to train the data, and experimental results show that the accuracy can reach more than 97\%, which corroborates the effectiveness of the proposed algorithm.
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT
Real-Time and Embedded Computing Systems Research Centre (CISTER)（实时与嵌入式计算系统研究中心）；TU Delft（代尔夫特理工大学）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（联邦科学与工业研究组织）；Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
Federated Learning for Industrial Internet of Things in Future Industries
Deakin University（迪肯大学）；CSIRO（联邦科学与工业研究组织）；University of New South Wales（新南威尔士大学）；Nanjing University of Science and Technology（南京理工大学）；Nanyang Technological University（南洋理工大学）；Princeton University（普林斯顿大学）
Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing
Nanjing University of Science and Technology（南京理工大学）；Zhejiang Lab（之江实验室）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC联合学院）；Louisiana State University（路易斯安那州立大学）
Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness
Mepco Schlenk Engineering College（梅普科施伦克工程学院）；Fordham University（福特汉姆大学）；University of South Florida（南佛罗里达大学）；Hung Yen University of Technology and Education（兴安工业大学）；Royal Military College of Canada（加拿大皇家军事学院）
Decentralized Rank Scheduling for Energy-Constrained Multi-Task Federated Fine-Tuning in Edge-Assisted IoV Networks
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Cyber Science and Technology, Sun Yat-sen University（中山大学网络安全科学与技术学院）
Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology)（广西智能网络与场景系统重点实验室（信息与通信学院，桂林电子科技大学））；National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology (Guangxi)（综合交通运输大数据应用技术国家工程实验室（广西））；School of Automation, Guangdong University of Technology（自动化学院，广东工业大学）；Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning（广西多媒体通信与网络技术重点实验室，南宁）；School of Computer and Electronic Information, Guangxi University（计算机与电子信息学院，广西大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
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）
Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networks
Department of Communication Engineering, National Central University, Taoyuan, Taiwan（国立中央大学通信工程系）；Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan（阳明交通大学电子工程系）；Next Generation Wireless, University of Southampton, Southampton, UK（南安普顿大学下一代无线技术）；Next-generation Communications Research Center, Hon Hai Research Institute, Taipei, Taiwan（富士康研究所下一代通讯研究中心）
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（Simula都市数字工程中心）
Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子工程与光电技术学院）；School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；School of Computer Engineering, Jiangsu Second Normal University（江苏第二师范学院计算机工程学院）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Department of Electronics Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；Digital Research Institute, ENN Group（新奥集团数字研究院）
Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series
Department of Electrical and Electronic Engineering, Imperial College London（电子工程系，帝国理工学院伦敦分校）；Centre for Quantum Engineering, Science and Technology (QuEST), Imperial College London（量子工程、科学与技术中心（QuEST），帝国理工学院伦敦分校）；Graduate Institute of Applied Physics, National Taiwan University（应用物理研究所，国立台湾大学）
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Department of Electronic Engineering, State Key laboratory of Space Network and Communications, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学电子工程系、空间网络与通信国家重点实验室、北京信息科学与技术国家研究中心）；Department of Computer Science, Brunel University（布鲁内尔大学计算机科学系）；Department of Electronic Engineering, Shanghai JiaoTong University（上海交通大学电子工程系）；School of Internet of Things Engineering, Wuxi Institute of Technology（无锡职业技术学院物联网工程学院）
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）
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
Heilongjiang University（黑龙江大学）；École de technologie supérieure, Université du Québec（魁北克大学高等技术学院）；Nanyang Technological University（南洋理工大学）；Wuhan University of Technology（武汉理工大学）；Alibaba-NTU Joint Research Institute（阿里巴巴-南洋理工大学联合研究院）
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing
AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Center for Intelligent Networking and Communications (CINC), University of Electronic Science and Technology of China（电子科技大学智能组网与通信中心）
Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Alibaba Group（阿里巴巴集团）；The Pennsylvania State University（宾夕法尼亚州立大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（信息与通信工程学院，北京邮电大学）；School of Computing, Macquarie University（计算学院，麦考瑞大学）；Department of Broadband Communication, Peng Cheng Laboratory（宽带通信部，鹏城实验室）；China Telecom, Sichuan Branch（中国电信四川分公司）
Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins
Department of Electrical and Computer Engineering, McMaster University, CANADA（电气与计算机工程系，麦斯特大学）；Department of Computing and Software, McMaster University, CANADA（计算与软件系，麦斯特大学）
