Federated Learning (FL) allows for collaboratively aggregating learned information across several computing devices and sharing the same amongst them, thereby tackling issues of privacy and the need of huge bandwidth. FL techniques generally use a central server or cloud for aggregating the models received from the devices. Such centralized FL techniques suffer from inherent problems such as failure of the central node and bottlenecks in channel bandwidth. When FL is used in conjunction with connected robots serving as devices, a failure of the central controlling entity can lead to a chaotic situation. This paper describes a mobile agent based paradigm to decentralize FL in multi-robot scenarios. Using Webots, a popular free open-source robot simulator, and Tartarus, a mobile agent platform, we present a methodology to decentralize federated learning in a set of connected robots. With Webots running on different connected computing systems, we show how mobile agents can perform the task of Decentralized Federated Reinforcement Learning (dFRL). Results obtained from experiments carried out using Q-learning and SARSA by aggregating their corresponding Q-tables, show the viability of using decentralized FL in the domain of robotics. Since the proposed work can be used in conjunction with other learning algorithms and also real robots, it can act as a vital tool for the study of decentralized FL using heterogeneous learning algorithms concurrently in multi-robot scenarios.
Federated Single-Agent Robotics: Multi-Robot Coordination Without Intra-Robot Multi-Agent Fragmentation
School of Software, Harbin Institute of Technology（哈尔滨工业大学软件学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；School of Mathematical and Computer Sciences, Heriot-Watt University, Malaysia Campus（赫瑞-沃顿大学马来西亚校区数学与计算机科学学院）；School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong University of Science and Technology（香港科技大学）
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong University of Science and Technology（香港科技大学）
SwiftBot: A Decentralized Platform for LLM-Powered Federated Robotic Task Execution
California State University, Long Beach, USA（加州州立大学长滩分校）；Case Western Reserve University, USA（凯斯西储大学）；University of Colorado Denver, USA（科罗拉多大学丹佛分校）；Montana State University, USA（蒙塔那州立大学）；University of Houston Clear Lake, USA（休斯顿大学clear lake分校）
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong University of Science and Technology（香港科技大学）；University of Macau（澳门大学）
Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains
Lab of Systems Engineering and Robotics (LASER), Department of Computer Systems, Universidade Federal da Paraiba（系统工程与机器人实验室（LASER），计算机系统系，巴伊帕巴联邦大学）；Department of Cybernetics, Czech Technical University in Prague（控制论系，布拉格捷克技术大学）
FedRobo: Federated Learning Driven Autonomous Inter Robots Communication For Optimal Chemical Sprays
University of Asia Pacific（亚太大学）；University of North Texas（北得克萨斯大学）；Green University of Bangladesh（孟加拉国绿色大学）；Southern Illinois University Carbondale（南伊利诺伊大学卡本代尔分校）
Federated Joint Learning of Robot Networks in Stroke Rehabilitation
College of Aeronautics and Engineering, Kent State University（肯特州立大学航空与工程学院）；Kent State University（肯特州立大学）；New Jersey Institute of Technology（新泽西理工学院）；Cleveland Clinic（克利夫兰诊所）
Personalized Mental State Evaluation in Human-Robot Interaction using Federated Learning
ARM-Lab University of Applied Sciences of Southern Switzerland (SUPSI)（ARM实验室 瑞士南方应用科学大学（SUPSI））；DAUIN Department Politecnico di Torino（DAUIN部门 托斯纳理工学院）；DIST Department Politecnico di Torino（DIST部门 托斯纳理工学院）
User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments
Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH)（计算机科学研究所，研究与技术基金会-希腊）；Computer Science Department (CSD), University of Crete (UoC)（克里特大学计算机科学系）
Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation
Analytics Everywhere Lab, University of New Brunswick, Canada（分析无界实验室，新不伦瑞克大学，加拿大）；SE+AI Research Lab, University of New Brunswick, Canada（SE+AI研究实验室，新不伦瑞克大学，加拿大）；National Research Council, New Brunswick, Canada（国家研究理事会，新不伦瑞克，加拿大）；RMIT University, Australia（皇家墨尔本理工大学，澳大利亚）
Federated Trust for Embodied Robot Capability Marketplaces
School of Software, Harbin Institute of Technology（哈尔滨工业大学软件学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）
Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电气与电子工程系）；School of Automation and Electrical Engineering, University of Science and Technology Beijing（北京科技大学自动化与电气工程学院）；Zhejiang Lab（浙江实验室）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）
Path Planning through Multi-Agent Reinforcement Learning in Dynamic Environments
Department of Computer Science University of Antwerp (UA)（安特卫普大学计算机科学系）；Department of Computer Engineering, Faculty of Engineering, Ayatollah Boroujerdi University（阿亚图拉·博鲁杰尔迪大学工程学院计算机工程系）；Department of Computer Science University of Antwerp (UA) and Flanders Make（安特卫普大学计算机科学系和弗拉芒制造）
