Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks
Singapore University of Technology and Design（新加坡科技设计大学）；Zhejiang University/University of Illinois at Urbana-Champaign Institute, Zhejiang University（浙江大学伊利诺伊大学厄巴纳-香槟校区联合学院，浙江大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Electronic Science and Technology of China（电子科技大学）；The University of Exeter（埃克塞特大学）
Chenyuan Feng, Howard H. Yang, Deshun Hu, Zhiwei Zhao, Tony Q. S. Quek, Geyong Min
Implementing federated learning (FL) algorithms in wireless networks has garnered a wide range of attention. However, few works have considered the impact of user mobility on the learning performance. To fill this research gap, firstly, we develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless networks where the mobile users may roam across multiple edge access points, leading to incompletion of inconsistent FL training. Secondly, we provide the convergence analysis of HFL with user mobility. Our analysis proves that the learning performance of HFL deteriorates drastically with highly-mobile users. And this decline in the learning performance will be exacerbated with small number of participants and large data distribution divergences among local data of users. To circumvent these issues, we propose a mobility-aware cluster federated learning (MACFL) algorithm by redesigning the access mechanism, local update rule and model aggregation scheme. Finally, we provide experiments to evaluate the learning performance of HFL and our MACFL. The results show that our MACFL can enhance the learning performance, especially for three different cases, namely, the case of users with non-independent and identical distribution data, the case of users with high mobility, and the cases with a small number of users.
The Role of Federated Learning in a Wireless World with Foundation Models
Singapore University of Technology and Design（新加坡科技设计大学）；Zhejiang University/University of Illinois Urbana-Champaign Institute（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究院）；National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）；National University of Singapore（新加坡国立大学）
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；Beijing Jiaotong University（北京交通大学）；OPPO；Imperial College London（伦敦帝国理工学院）
Hierarchical Federated Learning for Social Network with Mobility
Department of Electronic Engineering, Shanghai Jiao Tong University（电子工程系，上海交通大学）；School of Information Science and Engineering, Southeast University（信息科学与工程学院，东南大学）；Data61, CSIRO（Data61，CSIRO）；Pillar of Information Systems Technology and Design, Singapore University of Technology and Design（信息系统技术与设计 pillar，新加坡科技设计大学）
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles
Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室）；Information Technology R&D Center, Mitsubishi Electric Corporation（三菱电机株式会社信息技术研发中心）；NC State University（北卡罗来纳州立大学）
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification
Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University（北京信息科学与技术国家研究中心，电子工程系，清华大学）；School of Electronic and Information Engineering, Beijing Jiaotong University（电子与信息工程学院，北京交通大学）
A Novel Collaborative Framework for Efficient Synchronization in Split Federated Learning over Wireless Networks
Department of Electrical and Computer Engineering, Concordia University（电气与计算机工程系，康科迪亚大学）；Department of Physics and Computer Science, Faculty of Science, Wilfrid Laurier University（物理与计算机科学系，科学学院，威尔弗里德·劳里埃大学）
Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning
Texas A&M University（德克萨斯农工大学）；Texas A&M University at Qatar（德克萨斯农工大学卡塔尔分校）；Qatar Foundation Research, Development and Innovation RDI（卡塔尔基金会研究、开发与创新（RDI））；Tennessee Tech University（田纳西理工大学）
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
Department of Electrical and Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）；Department of Engineering, King’s College London（伦敦国王学院工程系）；Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications（北京邮电大学网络系统架构与融合北京市重点实验室）；Shenzhen Research Institute of Big Data and Future Network of Intelligence Institute, the Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）智能科学与技术研究院未来网络与大数据研究院深圳分院）
Towards Scalable Wireless Federated Learning: Challenges and Solutions
ShanghaiTech University（上海科技大学）；Nanjing University（南京大学）；Beihang University（北京航空航天大学）；Xiameng University（厦门大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence
School of Informatics, Xiamen University, China（厦门大学信息学院，中国）；Internet of Things Thrust, Hong Kong University of Science and Technology (Guangzhou), China（香港科技大学（广州）物联网研究组，中国）
Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data
Beijing University of Posts and Telecommunications（北京邮电大学）；Jiaxing University（嘉兴学院）；Northwestern Polytechnical University（西北工业大学）；Southeast University（东南大学）
Federated Latent Factor Learning for Recovering Wireless Sensor Networks Signal with Privacy-Preserving
College of Computer and Information Science（计算机与信息科学学院）；Southwest University（西南大学）；School of Engineering（工程学院）；University of Western Australia（西澳大学）；College of Food Science（食品科学学院）；Chongqing True Flavor Food Co LTD（重庆真味食品有限公司）
