mlsanityData to 7 Oct 2026

Federated Transfer Reinforcement Learning for Autonomous Driving

arXiv 1910.06001 ↗First posted 14 Oct 2019cs.LG cs.AI cs.ROcited by 43

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Abstract (arXiv)

Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL models typically involves in a multi-step process: pre-training RL models on simulators, uploading the pre-trained model to real-life robots, and fine-tuning the weight parameters on robot vehicles. This sequential process is extremely time-consuming and more importantly, knowledge from the fine-tuned model stays local and can not be re-used or leveraged collaboratively. To tackle this problem, we present an online federated RL transfer process for real-time knowledge extraction where all the participant agents make corresponding actions with the knowledge learned by others, even when they are acting in very different environments. To validate the effectiveness of the proposed approach, we constructed a real-life collision avoidance system with Microsoft Airsim simulator and NVIDIA JetsonTX2 car agents, which cooperatively learn from scratch to avoid collisions in indoor environment with obstacle objects. We demonstrate that with the proposed framework, the simulator car agents can transfer knowledge to the RC cars in real-time, with 27% increase in the average distance with obstacles and 42% decrease in the collision counts.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Oct 2019.

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Citing papers per year: 2021: 8; 2022: 5; 2023: 17; 2024: 8; 2025: 3; 2026: 2.

43 papers.

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  1. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  2. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
  3. 2021Fault-Tolerant Federated Reinforcement Learning with Theoretical Guaranteecited by 52
  4. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  5. 2022FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergencecited by 29
  6. 2024Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learningcited by 29
  7. 2024Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimizationcited by 28
  8. 2023GradientCoin: A Peer-to-Peer Decentralized Large Language Modelscited by 23
  9. 2022Federated Adversarial Learning: A Framework with Convergence Analysiscited by 17
  10. 2021Provably Efficient Multi-Task Reinforcement Learning with Model Transfercited by 16
  11. 2022FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregationcited by 16
  12. 2021FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysiscited by 13
31 more
  1. 2021Failure Prediction in Production Line Based on Federated Learning: An Empirical Studycited by 11
  2. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  3. 2023Deep Transfer Learning for Intelligent Vehicle Perception: a Surveycited by 10
  4. 2023Federated Empirical Risk Minimization via Second-Order Methodcited by 9
  5. 2023Handling Data Heterogeneity via Architectural Design for Federated Visual Recognitioncited by 8
  6. 2023Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Drivingcited by 7
  7. 2022Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learningcited by 6
  8. 2023FedHQL: Federated Heterogeneous Q-Learningcited by 6
  9. 2023Local Environment Poisoning Attacks on Federated Reinforcement Learningcited by 6
  10. 2021The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communicationcited by 5
  11. 2025FOVA: Offline Federated Reinforcement Learning with Mixed-Quality Datacited by 5
  12. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  13. 2024Federated Learning under Partially Class-Disjoint Data via Manifold Reshapingcited by 4
  14. 2023Client Selection for Federated Policy Optimization with Environment Heterogeneitycited by 3
  15. 2024Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Reviewcited by 3
  16. 2023Federated Learning over a Wireless Network: Distributed User Selection through Random Accesscited by 2
  17. 2024Combining Federated Learning and Control: A Surveycited by 2
  18. 2023Policy Resilience to Environment Poisoning Attacks on Reinforcement Learningcited by 1
  19. 2023DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learningcited by 1
  20. 2023Federated Learning on Edge Sensing Devices: A Reviewcited by 1
  21. 2024Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiencycited by 1
  22. 2024Federated Offline Policy Optimization with Dual Regularizationcited by 1
  23. 2022AVDDPG: Federated reinforcement learning applied to autonomous platoon controlcited by 0
  24. 2023Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfercited by 0
  25. 2023Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic Classificationcited by 0
  26. 2023Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learningcited by 0
  27. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  28. 2025Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learningcited by 0
  29. 2025Lightweight Temporal Transformer Decomposition for Federated Autonomous Drivingcited by 0
  30. 2026FedQHD: Closed-Form Function-Space Federated Reinforcement Learningcited by 0
  31. 2026FedGuide: Diffusion Prior Alignment and Value Baseline Guidance for Heterogeneous Federated Reinforcement Learningcited by 0