Federated RL

RL: Reinforcement Learning

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 21

All topics

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
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
Spatiotemporal Continual Federated Learning for Agentic Multi-UAV Edge Networks: Mitigating Catastrophic Forgetting
Jan 29, 2026Chuan-Chi LaiMulti-Objective Reinforcement Learning Federated RL
Department of Communications Engineering, National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan · Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI), National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan

Aug 31, 2026, stat.MLJ/K move · Enter open · S save

Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost

Zihang Liang, Haochen Zhang, Lingzhou Xue

Department of Statistics, The Pennsylvania State University

Reinforcement Learning Federated RL Multi-Agent Reinforcement Learning Federated Learning

Open paper AskPDF arXiv

We study federated online reinforcement learning with linear function approximation. While recent multi-agent reinforcement learning algorithms achieve strong regret guarantees, they typically require sharing raw trajectories. This reliance incurs a communication cost that scales linearly with the number of episodes and violates the privacy constraints of federated settings. To address these limitations, we propose Fed-LSVI, the first provably efficient federated algorithm for online reinforcement learning with linear function approximation in episodic Markov decision processes. By integrating a determinant-based event-triggered synchronization with a stepwise backward update mechanism, Fed-LSVI enables agents to collaboratively learn an optimal policy by exchanging only compressed sufficient statistics. We prove that Fed-LSVI achieves a regret bound of

\widetilde{\mathcal O}(\sqrt{Md^3H^4T})

, where

d

is the feature dimension,

H

is the horizon length,

M

is the number of agents, and

T

is the number of episodes per agent, matching the best-known regret for multi-agent online reinforcement learning with linear function approximation. Moreover, by following the stringent communication and privacy constraints of the federated setting, Fed-LSVI reduces the communication cost to only logarithmic dependence on

T

, representing a significant improvement over prior methods.

Momentum

Related topics

Latest papers 21