Federated RL

RL: Reinforcement Learning

Federated reinforcement learning studies how agents can improve decision-making together by sharing model updates rather than raw trajectories. Common approaches adapt federated averaging, actor-critic and Q-learning methods, and add personalization or meta-learning when agents face different environments; decentralized aggregation, robust statistics, and compressed or event-triggered communication address other practical constraints. Applications include robotics and UAV teams, wireless and edge resource allocation, autonomous vehicles, and energy systems, often evaluated in control benchmarks or network simulations. A central challenge is balancing shared learning with local adaptation while handling non-IID data, limited communication, unreliable participants, and safety constraints; theoretical guarantees are developing, but real-world deployment remains difficult to establish.

Latest papers

34 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Movable-Element Multi-Functional RIS-Assisted Hybrid RSMA-NOMA: A Constrained Hybrid Reinforcement Learning Approach
Oct 26, 2026Li-Hsiang Shen, Chi-Te KuoReconfigurable Intelligent Surfaces Wireless Resource Allocation
Department of Communication Engineering, National Central University, Taoyuan 320317, Taiwan
When to Intervene? State-Aware Sparse Manipulation in Federated Reinforcement Learning
Oct 8, 2026Shutong Zheng, Sijia ChenFederated RL Federated Learning
Sun Yat-sen University · The Hong Kong University of Science and Technology (Guangzhou)
Fed-GRPO: Reward-Signal-Driven Federated Group Relative Policy Optimization
Oct 8, 2026Pengxin Guo, Shuang Zeng, Zonggen Li +3Federated RL RL for Language Model Reasoning
The University of Hong Kong · Sun Yat-sen University
Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning
Oct 2, 2026Sreejeet Maity, Aritra MitraFederated RL Byzantine-Robust Federated Learning
Department of Electrical and Computer Engineering North Carolina State University, Raleigh
Personalized Federated Reinforcement Learning via Model-Agnostic Meta-Learning: Convergence of Exact and Hessian-Free Meta-Policy Gradients
Sep 19, 2026Ali Beikmohammadi, Sarit Khirirat, Sindri MagnússonFederated RL Meta-Learning
Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden. · Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand.
Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost
Aug 31, 2026Zihang Liang, Haochen Zhang, Lingzhou XueReinforcement Learning Federated RL
Department of Statistics, The Pennsylvania State University
Robust Federated Q-Learning with Almost No Communication
Aug 24, 2026Sreejeet Maity, Aritra MitraFederated RL Q-Learning
Department of Electrical and Computer Engineering, North Carolina State University
Exploration-Driven Personalized Federated Reinforcement Learning via Intrinsic Motivation
Aug 11, 2026Md Rafid Islam, Rafsan Jany, Zahid Hasan +1Federated RL Intrinsic Motivation
Department of Electrical and Computer Engineering North South University Dhaka, Bangladesh · Digital Health Research Division Korea Institute of Oriental Medicine Daejeon, South Korea · Department of Electrical and Computer Engineering The University of Alabama in Huntsville Huntsville, AL, USA
FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs
Aug 4, 2026Amin Farajzadeh, Melike Erol-KantarciFederated RL 6G Networks
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada · with the NETCORE Lab
FedGuide: Diffusion Prior Alignment and Value Baseline Guidance for Heterogeneous Federated Reinforcement Learning
Jul 22, 2026Zhilin He, Gauri JoshiFederated RL Federated Learning
Carnegie Mellon University
Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination
Jul 14, 2026Usman Haider, Karl MasonFederated Learning Aggregation Federated RL
School of Computer Science, University of Galway, Ireland.
Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning
Jul 9, 2026Zifan Zhang, Minghong Fang, Dianwei Chen +5Federated RL Autonomous Driving
North Carolina State University, USA · University of Louisville, USA · University of Maryland, USA +2
Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids
Jul 6, 2026Omar Al-Refai, Ibrahim Shahbaz, Adam Ali Husseinat +1Reinforcement Learning Federated RL
iSTAR Lab, Texas A&M University College Station, TX, USA
Privacy-Preserving Federated Temporal Graph Learning with Digital Twin--Guided Adaptive Deception for Cyber-Resilient IoMT
Jun 19, 2026Syed Zeeshan Haider, Anwar Shah, Muneeb Arif +2Federated RL Autonomous Cyber Defense
FAST National University of Computer and Emerging Sciences, Islamabad, Pakistan
Federated Temporal Attention Intelligence for Cyber-Resilient IoMT: Lightweight Digital Twins and PPO-Driven Honeypot Deception
Jun 19, 2026Syed Zeeshan Haider, Anwar Shah, Muneeb Arif +2Federated RL Autonomous Cyber Defense
FAST National University of Computer and Emerging Sciences, Islamabad, Pakistan
FGRPO: Federated GRPO with Adaptive Aggregation on Non-IID Data
Jun 2, 2026Pengyu Chen, Shaowei Li, Kai Wang +4Federated Learning Aggregation Federated RL
School of Computer Science and Technology, Shandong University, Qingdao China · School of Mathematical Science, Peking University, China · School of Computer Science and Artificial Intelligence, Shanghai University of Finance and Economics, Shanghai, China +1
FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
May 27, 2026Yuchen Hou, Yongshan Chen, Zhuowen Zou +4RL Control Federated Learning Aggregation
Northeastern University · University of California, Irvine · The George Washington University
FedCritic: Serverless Federated Critic Learning-based Resource Allocation for Multi-Cell OFDMA in 6G
May 20, 2026Amin Farajzadeh, Melike Erol-KantarciFederated RL 6G Networks
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach
May 18, 2026Yaorong Huang, Jingtao Luo, Xuechao WangFederated RL Edge Computing
∗The Hong Kong University of Science and Technology (Guangzhou) · †Chengdu Neusoft University
Collaborative Yet Personalized Policy Training: Single-Timescale Federated Actor-Critic
May 14, 2026Leo Muxing Wang, Pengkun Yang, Lili SuFederated RL Actor-Critic Methods
Northeastern University · Tsinghua University
Reinforcement Learning for Scalable and Trustworthy Intelligent Systems
May 8, 2026Guangchen LanFederated RL Privacy-Preserving Language Models
Elmore Family School of Electrical and Computer Engineering, West Lafayette, Indiana · THE PURDUE UNIVERSITY GRADUATE SCHOOL
MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria
May 6, 2026Xuwei Yang, David B. Emerson, Fatemeh Tavakoli +1Nash Equilibrium Federated RL
Department of Mathematics and Statistics McMaster University Ontario, Canada · Vector Institute Ontario, Canada
Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information
May 4, 2026Sumedh J. Dongare, Patrick Weber, Andrea Ortiz +3Federated RL Decentralized MARL
Communications Engineering Lab at the Technical University of Darmstadt, Germany · Chair of Information Systems and Information Management at the Goethe University Frankfurt, Germany · Institute of Telecommunications at the Vienna University of Technology, Austria
Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments
May 4, 2026Qinwei Huang, Rui Zuo, Simon Khan +1Hierarchical Federated Learning Federated RL
EECS Syracuse University Syracuse, USA · AFRL Air Force Research Laboratory Rome, USA
PALCAS: A Priority-Aware Intelligent Lane Change Advisory System for Autonomous Vehicles using Federated Reinforcement Learning
Apr 29, 2026Yassine Ibork, Nhat Ha Nguyen, Myounggyu Won +1Federated RL Autonomous Driving
School of Computing, Wichita State University · Department of Computer Science, University of Memphis
Efficient Federated RLHF via Zeroth-Order Policy Optimization
Apr 20, 2026Deyi Wang, Qining Zhang, Lei YingFederated RL Communication-Efficient Distributed Training
University of Michigan, Ann Arbor
PubSwap: Public-Data Off-Policy Coordination for Federated RLVR
Apr 14, 2026Anupam Nayak, Baris Askin, Muhammed Ustaomeroglu +2Federated RL Reinforcement Learning with Verifiable Rewards
Carnegie Mellon University
Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity
Apr 10, 2026Yiran Pang, Zhen Ni, Xiangnan ZhongFederated RL Heterogeneous Federated Learning
Department of Electrical Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, USA
Federated Distributional Reinforcement Learning with Distributional Critic Regularization
Mar 18, 2026David Millard, Cecilia Alm, Rashid Ali +2Federated RL Distributional RL
Dept. of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA · Dept. of Psychology and School of Information, Rochester Institute of Technology, Rochester, NY 14623, USA · Department of Engineering Science, University West, Trollhättan 461 30, Sweden +1
DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning
Mar 16, 2026Zhiyu Wang, Mohammad Goudarzi, Mingming Gong +1Federated RL Non-IID Federated Learning
The Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia · The Faculty of Information Technology, Monash University, Australia · School of Mathematics and Statistics, The University of Melbourne, Australia

Oct 26, 2026J/K move · Enter open · S save

Movable-Element Multi-Functional RIS-Assisted Hybrid RSMA-NOMA: A Constrained Hybrid Reinforcement Learning Approach

Li-Hsiang Shen, Chi-Te Kuo

Department of Communication Engineering, National Central University, Taoyuan 320317, Taiwan

Reconfigurable Intelligent Surfaces Wireless Resource Allocation Hybrid Action Space RL Constrained MARL Federated RL

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In this work, we propose a movable-element (ME) multi-functional reconfigurable intelligent surface (MF-RIS) architecture, termed MM-RIS, which integrates element mobility with adaptive signal reflection, transmission, amplification, and energy harvesting (EH) to enhance communication flexibility and energy self-sustainability. A hybrid rate-splitting multiple access (RSMA) and non-orthogonal multiple access (NOMA) transmission scheme is further designed to improve spectral efficiency. We formulate an energy efficiency (EE) maximization problem by jointly optimizing transmit beamforming, power and rate allocation, MM-RIS configurations, panel deployment, and ME positions. To address the resulting non-convex mixed continuous-discrete optimization problem, we propose constrained hybrid deep reinforcement learning with federated transformer and parametrized sharing (CRAFTS). CRAFTS integrates multi-agent proximal policy optimization (MAPPO) and multi-agent deep Q-networks (MADQN) through bi-directional parametrized sharing to handle continuous and discrete actions, respectively. Moreover, a transformer-based architecture and federated learning (FL) are incorporated to enhance state representation and distributed knowledge sharing, whilst a Lagrangian-based adaptive penalty mechanism facilitates the constraint satisfaction. Simulation results have demonstrated that the proposed MM-RIS architecture and CRAFTS framework substantially improve EE and constraint satisfaction compared to the benchmarks in the open literature. Moreover, the results validate the benefits of spatially reconfigurable MM-RIS, hybrid RSMA-NOMA transmissions, and intelligent collaborative learning, highlighting the potential of the proposed framework for energy-efficient and adaptive next-generation wireless networks.