mlsanityData to 7 Oct 2026

Deep Federated Learning for Autonomous Driving

arXiv 2110.05754 ↗First posted 12 Oct 2021cs.LG cs.DC cs.ROcited by 54

On the map: End-to-end autonomous driving → End-to-end system architecture

Abstract (arXiv)

Autonomous driving is an active research topic in both academia and industry. However, most of the existing solutions focus on improving the accuracy by training learnable models with centralized large-scale data. Therefore, these methods do not take into account the user's privacy. In this paper, we present a new approach to learn autonomous driving policy while respecting privacy concerns. We propose a peer-to-peer Deep Federated Learning (DFL) approach to train deep architectures in a fully decentralized manner and remove the need for central orchestration. We design a new Federated Autonomous Driving network (FADNet) that can improve the model stability, ensure convergence, and handle imbalanced data distribution problems while is being trained with federated learning methods. Intensively experimental results on three datasets show that our approach with FADNet and DFL achieves superior accuracy compared with other recent methods. Furthermore, our approach can maintain privacy by not collecting user data to a central server.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 43 of its 63 references that are in the map. First posted on arXiv 12 Oct 2021.

Builds on

43 of 63 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Driving19 Apr

Other references · 42

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  5. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  6. 2019Advances and Open Problems in Federated Learningcited by 3,021
  7. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  8. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  9. 2016End to End Learning for Self-Driving Carscited by 1,000
  10. 2019The Replica Dataset: A Digital Replica of Indoor Spacescited by 885
  11. 2013Network In Networkcited by 600
  12. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
30 more
  1. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  2. 2016Learning to Navigate in Complex Environmentscited by 192
  3. 2016End-to-end Learning of Driving Models from Large-scale Video Datasetscited by 147
  4. 2018Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithmscited by 106
  5. 2018Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learningcited by 91
  6. 2021Ground-aware Monocular 3D Object Detection for Autonomous Drivingcited by 65
  7. 2016Deep Reinforcement Learning with Successor Features for Navigation across Similar Environmentscited by 50
  8. 2020Decentralized Federated Learning via Mutual Knowledge Transfercited by 42
  9. 2021MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Drivingcited by 41
  10. 2016From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robotscited by 37
  11. 2020Throughput-Optimal Topology Design for Cross-Silo Federated Learningcited by 37
  12. 2018Variational End-to-End Navigation and Localizationcited by 34
  13. 2020Shared Cross-Modal Trajectory Prediction for Autonomous Drivingcited by 31
  14. 2021Coarse-to-Fine Reasoning for Visual Question Answeringcited by 25
  15. 2021Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehiclescited by 24
  16. 2021Binary TTC: A Temporal Geofence for Autonomous Navigationcited by 20
  17. 2017Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awarenesscited by 17
  18. 2017End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependenciescited by 16
  19. 2020Autonomous Navigation in Complex Environments with Deep Multimodal Fusion Networkcited by 16
  20. 2021Real-time End-to-End Federated Learning: An Automotive Case Studycited by 15
  21. 2021Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Drivingcited by 15
  22. 2019Self-Driving Car Steering Angle Prediction Based on Image Recognitioncited by 14
  23. 2021Light-weight Deformable Registration using Adversarial Learning with Distilling Knowledgecited by 12
  24. 2021A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Carscited by 6
  25. 2020Turn Signal Prediction: A Federated Learning Case Studycited by 5
  26. 2019Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Datacited by 4
  27. 2019Scene Understanding for Autonomous Manipulation with Deep Learningcited by 3
  28. 2020Autonomous Navigation with Mobile Robots using Deep Learning and the Robot Operating Systemcited by 2
  29. 2018Object-oriented Targets for Visual Navigation using Rich Semantic Representationscited by 1
  30. 2020Hybrid Data-Driven and Analytical Model for Kinematic Control of a Surgical Robotic Toolcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 21; 2024: 17; 2025: 6; 2026: 3.

54 papers.

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  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2022FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Drivingcited by 41
  4. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  5. 2023Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Modelcited by 33
  6. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  7. 2023Federated Learning for Computer Visioncited by 22
  8. 2023Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognitioncited by 19
  9. 2024Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baselinecited by 17
  10. 2023FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systemscited by 14
  11. 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
  12. 2023FedWon: Triumphing Multi-domain Federated Learning Without Normalizationcited by 11
38 more
  1. 2023HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigationcited by 9
  2. 2023A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspectivecited by 9
  3. 2022Fine-Grained Visual Classification using Self Assessment Classifiercited by 8
  4. 2023Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Drivingcited by 7
  5. 2023Federated Online and Bandit Convex Optimizationcited by 7
  6. 2024Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraintscited by 7
  7. 2023Continual Adaptation of Vision Transformers for Federated Learningcited by 6
  8. 2023One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Modelscited by 5
  9. 2024Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Testscited by 5
  10. 2023On the Effect of Defections in Federated Learning and How to Prevent Themcited by 4
  11. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  12. 2024FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learningcited by 4
  13. 2024Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigationcited by 4
  14. 2022Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Surveycited by 3
  15. 2022Federated clustering with GAN-based data synthesiscited by 3
  16. 2024Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networkscited by 3
  17. 2024Optimizing Federated Learning by Entropy-Based Client Selectioncited by 3
  18. 2024Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passingcited by 2
  19. 2022Smart Multi-tenant Federated Learningcited by 1
  20. 2022Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topologycited by 1
  21. 2023Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigationscited by 1
  22. 2023An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processingcited by 1
  23. 2024A review on different techniques used to combat the non-IID and heterogeneous nature of data in FLcited by 1
  24. 2024On the Conflict of Robustness and Learning in Collaborative Machine Learningcited by 1
  25. 2024Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacycited by 1
  26. 2024FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimatorcited by 1
  27. 2024Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimizationcited by 1
  28. 2025Federated Causal Inference in Healthcare: Methods, Challenges, and Applicationscited by 1
  29. 2025FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaborationcited by 1
  30. 2022Detection and Prevention Against Poisoning Attacks in Federated Learningcited by 0
  31. 2023FTA: Stealthy and Adaptive Backdoor Attack with Flexible Triggers on Federated Learningcited by 0
  32. 2023Federated Active Learning for Target Domain Generalisationcited by 0
  33. 2024Poisoning Attacks on Federated Learning for Autonomous Drivingcited by 0
  34. 2024Impact of Network Topology on Byzantine Resilience in Decentralized Federated Learningcited by 0
  35. 2024Personalized Federated Learning for Cross-view Geo-localizationcited by 0
  36. 2025Bayesian Federated Learning for Continual Trainingcited by 0
  37. 2025Personalized Federated Learning under Model Dissimilarity Constraintscited by 0
  38. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0

50 most cited of 54 All 54 (JSON)

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  1. 2025FLAD: Federated Learning for LLM-based Autonomous Driving in Vehicle-Edge-Cloud Networks
  2. 2023Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
  3. 2025Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
  4. 2026Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning
  5. 2025FedRand: Enhancing Privacy in Federated Learning with Randomized LoRA Subparameter Updates
  6. 2021DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning
  7. 2024Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training
  8. 2024Attack End-to-End Autonomous Driving through Module-Wise Noise