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Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

arXiv 1901.06455 ↗First posted 19 Jan 2019cs.RO cs.AI cs.DCcited by 78

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Abstract (arXiv)

This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL.

Key facts. Cited by 78 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Jan 2019.

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Citing papers per year: 2019: 2; 2020: 0; 2021: 22; 2022: 10; 2023: 15; 2024: 17; 2025: 7; 2026: 5.

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  1. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  2. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 81
  3. 2022Federated Reinforcement Learning with Environment Heterogeneitycited by 63
  4. 2021Fault-Tolerant Federated Reinforcement Learning with Theoretical Guaranteecited by 54
  5. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  6. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 39
  7. 2024Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learningcited by 31
  8. 2022FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergencecited by 29
  9. 2023Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneitycited by 27
  10. 2023Federated Domain Generalization: A Surveycited by 25
  11. 2019Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Datacited by 24
  12. 2021Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehiclescited by 24
38 more
  1. 2021Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximationcited by 23
  2. 2024Unleashing the Power of Continual Learning on Non-Centralized Devices: A Surveycited by 23
  3. 2025A Survey of Continual Reinforcement Learningcited by 18
  4. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  5. 2021Provably Efficient Multi-Task Reinforcement Learning with Model Transfercited by 16
  6. 2021Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthinesscited by 15
  7. 2021Decentralized Unsupervised Learning of Visual Representationscited by 15
  8. 2021Multi-Task Federated Reinforcement Learning with Adversariescited by 13
  9. 2024FedRLHF: A Convergence-Guaranteed Federated Framework for Privacy-Preserving and Personalized RLHFcited by 12
  10. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  11. 2021DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systemscited by 10
  12. 2023Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximationcited by 9
  13. 2021Federated Learning for Privacy-Preserving Open Innovation Future on Digital Healthcited by 8
  14. 2024Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environmentscited by 8
  15. 2025Provably Robust Federated Reinforcement Learningcited by 8
  16. 2021User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environmentscited by 7
  17. 2022Federated Reinforcement Learning for Collective Navigation of Robotic Swarmscited by 7
  18. 2023Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devicescited by 7
  19. 2023Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Drivingcited by 7
  20. 2021FLSys: Toward an Open Ecosystem for Federated Learning Mobile Appscited by 6
  21. 2021Personalized Federated Learning of Driver Prediction Models for Autonomous Drivingcited by 6
  22. 2023FedHQL: Federated Heterogeneous Q-Learningcited by 6
  23. 2023Local Environment Poisoning Attacks on Federated Reinforcement Learningcited by 6
  24. 2021Sparse Personalized Federated Learningcited by 5
  25. 2022Federated Continual Learning for Socially Aware Roboticscited by 5
  26. 2019Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Datacited by 4
  27. 2021Homogeneous Learning: Self-Attention Decentralized Deep Learningcited by 4
  28. 2022Federated Meta-Learning for Traffic Steering in O-RANcited by 4
  29. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  30. 2024Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigationcited by 4
  31. 2024On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representationscited by 4
  32. 2022Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotioncited by 3
  33. 2024Combining Federated Learning and Control: A Surveycited by 3
  34. 2021Deep Learning for Embodied Vision Navigation: A Surveycited by 2
  35. 2021Federated Ensemble Model-based Reinforcement Learning in Edge Computingcited by 2
  36. 2023Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challengescited by 2
  37. 2024Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing Systemcited by 2
  38. 2024CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screeningcited by 2

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