mlsanityData to 14 Oct 2026

Federated Deep Reinforcement Learning

arXiv 1901.08277 ↗First posted 24 Jan 2019cs.LG cs.AIcited by 35

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Abstract (arXiv)

In deep reinforcement learning, building policies of high-quality is challenging when the feature space of states is small and the training data is limited. Despite the success of previous transfer learning approaches in deep reinforcement learning, directly transferring data or models from an agent to another agent is often not allowed due to the privacy of data and/or models in many privacy-aware applications. In this paper, we propose a novel deep reinforcement learning framework to federatively build models of high-quality for agents with consideration of their privacies, namely Federated deep Reinforcement Learning (FedRL). To protect the privacy of data and models, we exploit Gausian differentials on the information shared with each other when updating their local models. In the experiment, we evaluate our FedRL framework in two diverse domains, Grid-world and Text2Action domains, by comparing to various baselines.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jan 2019.

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Citing papers per year: 2021: 5; 2022: 8; 2023: 6; 2024: 9; 2025: 5; 2026: 2.

35 papers.

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  1. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  2. 2024Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learningcited by 31
  3. 2022Federated Neural Banditscited by 17
  4. 2021Multi-Task Federated Reinforcement Learning with Adversariescited by 13
  5. 2022Federated Offline Reinforcement Learningcited by 13
  6. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  7. 2022Differentially Private Graph Classification with GNNscited by 12
  8. 2023Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learningcited by 11
  9. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  10. 2023Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximationcited by 9
  11. 2022FedFormer: Contextual Federation with Attention in Reinforcement Learningcited by 8
  12. 2022Federated Reinforcement Learning for Collective Navigation of Robotic Swarmscited by 7
23 more
  1. 2023FedHQL: Federated Heterogeneous Q-Learningcited by 6
  2. 2024FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learningcited by 6
  3. 2024Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agentscited by 6
  4. 2021Network Support for High-performance Distributed Machine Learningcited by 4
  5. 2022PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communicationscited by 3
  6. 2023Provably Efficient Offline Reinforcement Learning with Perturbed Data Sourcescited by 3
  7. 2021Robust Convergence in Federated Learning through Label-wise Clusteringcited by 2
  8. 2024Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast Convergencecited by 2
  9. 2024CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screeningcited by 2
  10. 2025Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysiscited by 2
  11. 2025Convergence Guarantees for Federated SARSA with Local Training and Heterogeneous Agentscited by 2
  12. 2022Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learningcited by 1
  13. 2023Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learningcited by 1
  14. 2024Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiencycited by 1
  15. 2024Federated Offline Policy Optimization with Dual Regularizationcited by 1
  16. 2024FedSlate:A Federated Deep Reinforcement Learning Recommender Systemcited by 1
  17. 2025Parameter-Free Federated TD Learning with Markov Noise in Heterogeneous Environmentscited by 1
  18. 2026Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraintscited by 1
  19. 2022System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policycited by 0
  20. 2023Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validationscited by 0
  21. 2025Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learningcited by 0
  22. 2025Federated Reinforcement Learning in Heterogeneous Environmentscited by 0
  23. 2026Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learningcited by 0