We consider the problem of federated offline reinforcement learning (RL), a scenario under which distributed learning agents must collaboratively learn a high-quality control policy only using small pre-collected datasets generated according to different unknown behavior policies. Naïvely combining a standard offline RL approach with a standard federated learning approach to solve this problem can lead to poorly performing policies. In response, we develop the Federated Ensemble-Directed Offline Reinforcement Learning Algorithm (FEDORA), which distills the collective wisdom of the clients using an ensemble learning approach. We develop the FEDORA codebase to utilize distributed compute resources on a federated learning platform. We show that FEDORA significantly outperforms other approaches, including offline RL over the combined data pool, in various complex continuous control environments and real-world datasets. Finally, we demonstrate the performance of FEDORA in the real-world on a mobile robot. We provide our code and a video of our experiments at \url{https://github.com/DesikRengarajan/FEDORA}.
FOVA: Offline Federated Reinforcement Learning with Mixed-Quality Data
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；School of Cyber Science and Technology, Sun Yat-sen University（中山大学网络科学与技术学院）；Department of Computer Science and Technology, BNRist, Tsinghua University（清华大学计算机科学与技术系）；Zhongguancun Laboratory（中关村实验室）
FORLER: Federated Offline Reinforcement Learning with Q-Ensemble and Actor Rectification
Central South University, School of Computer Science, Changsha, China（中南大学计算机学院）；Sun Yat-sen University, School of Cyber Science and Technology, Shenzhen, China（中山大学网络安全学院）
School of Software, Shandong University, Jinan, China（软件学院，山东大学，济南，中国）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, China（人工智能研究联合中心（C-FAIR），山东大学，中国）；College of Computing and Data Science, Nanyang Technological University, Singapore（计算与数据科学学院，南洋理工大学，新加坡）
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System
Academy of Interdisciplinary Studies, The Hong Kong University of Science and Technology（香港科技大学跨学科研究学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）；School of Mathematical Sciences, Lancaster University（兰卡斯特大学数学科学学院）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）；Met Office Hadley Centre（英国气象局哈德利中心）
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning
Department of Mathematical Sciences, Politecnico di Torino（波兰理工学院数学科学系）；Istituto Nazionale di Fisica Nucleare, Sezione di Torino（意大利国家核物理研究所托斯卡纳分部）；Tynk S.R.L.（Tynk公司）；Laboratoire de Physique de l’Ecole Normale Supérieure, ENS Université PSL（巴黎高等科学研究院物理实验室）；CNRS, Sorbonne Université, Université de Paris（法国国家科学研究中心、索邦大学、巴黎大学）
LAFA: Agentic LLM-Driven Federated Analytics over Decentralized Data Sources
Shanghai Jiao Tong University（上海交通大学）；Peng Cheng Laboratory（鹏城实验室）；The University of Hong Kong（香港大学）；Zhejiang University（浙江大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Houston（休斯顿大学）
Sporadic Gradient Tracking over Directed Graphs: A Theoretical Perspective on Decentralized Federated Learning
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA（电气与计算机工程学院，普渡大学，西拉法基，印第安纳州，美国）；Department of Computer Science and Engineering, Yonsei University, Seoul, South Korea（计算机科学与工程系，延世大学，首尔，韩国）；Department of Electrical Engineering, University at Buffalo -- SUNY, Buffalo, NY, USA（电气工程系，布法罗大学——SUNY，布法罗，纽约州，美国）
Directed Temporal Representations for Offline Visual Control
University of Sheffield（谢菲尔德大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai University of Finance and Economics（上海财经大学）；University College London（伦敦大学学院）
Decentralized Directed Collaboration for Personalized Federated Learning
Nanjing University of Science and Technology（南京理工大学）；Tsinghua University（清华大学）；National University of Defense Technology（国防科技大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；JD Explore Academy（京东探索研究院）
Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networks
School of Electrical, Computer and Energy Engineering, Arizona State University（亚利桑那州立大学电气、计算机与能源工程学院）；Department of Electrical and Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Intelligent Transportation Thrust, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）智能交通枢纽）；School of Software, Nanjing University of Information Science and Technology（南京信息工程大学软件学院）；Zhongguancun Lab（中关村实验室）；School of Computing and Communications, Lancaster University（兰卡斯特大学计算与通信学院）；JD Intelligent Cities Research（京东智能城市研究院）；JD iCity, JD Technology（京东科技京东智城）
Accelerating Federated Edge Learning via Topology Optimization
The University of Hong Kong（香港大学）；Southern University of Science and Technology（南方科技大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Peng Cheng Laboratory（鹏城实验室）
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning
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（电子信息工程学院，北京交通大学）
