mlsanityData to 14 Oct 2026

Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving

arXiv 2306.16169 ↗First posted 28 Jun 2023cs.RO cs.DC cs.LGcited by 7

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

Abstract (arXiv)

While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server. Hierarchical federated learning (HFL) overcomes such drawbacks via introduction of mid-point edge servers. However, the orchestration between constrained communication resources and HFL performance becomes an urgent problem. This paper proposes an optimization-based Communication Resource Constrained Hierarchical Federated Learning (CRCHFL) framework to minimize the generalization error of the autonomous driving model using hybrid data and model aggregation. The effectiveness of the proposed CRCHFL is evaluated in the Car Learning to Act (CARLA) simulation platform. Results show that the proposed CRCHFL both accelerates the convergence rate and enhances the generalization of federated learning autonomous driving model. Moreover, under the same communication resource budget, it outperforms the HFL by 10.33% and the SFL by 12.44%.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 24 of its 36 references that are in the map. First posted on arXiv 28 Jun 2023.

Builds on

24 of 36 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Deep Federated Learning for Autonomous Driving12 Oct

Other references · 23

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  3. 2016End to End Learning for Self-Driving Carscited by 1,009
  4. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
  5. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  6. 2018HG-DAgger: Interactive Imitation Learning with Human Expertscited by 266
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  8. 2019Multimodal End-to-End Autonomous Drivingcited by 110
  9. 2019Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systemscited by 78
  10. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  11. 2019Federated Transfer Reinforcement Learning for Autonomous Drivingcited by 44
  12. 2021Rethinking gradient sparsification as total error minimizationcited by 41
11 more
  1. 2021Federated Learning with Domain Generalizationcited by 41
  2. 2019Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Drivingcited by 30
  3. 2019Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Datacited by 24
  4. 2021Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehiclescited by 24
  5. 2022Towards Efficient Communications in Federated Learning: A Contemporary Surveycited by 24
  6. 2021Real-time End-to-End Federated Learning: An Automotive Case Studycited by 15
  7. 2022Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verificationcited by 14
  8. 2020Hierarchical Federated Learning through LAN-WAN Orchestrationcited by 13
  9. 2020Sparse Communication for Training Deep Networkscited by 10
  10. 2020Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systemscited by 9
  11. 2022Lossy Gradient Compression: How Much Accuracy Can One Bit Buy?cited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 3.

7 papers.

  1. 2024Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Drivingcited by 8
  2. 2025Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectorycited by 5
  3. 2025FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Drivingcited by 5
  4. 2025FedDSR: Federated Deep Supervision and Regularization Towards Autonomous Drivingcited by 0
  5. 2026Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networkscited by 0
  6. 2026OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularizationcited by 0
  7. 2026AirMoE: Statistic-Augmented Over-the-Air MoE for Collaborative Intelligencecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025FLAD: Federated Learning for LLM-based Autonomous Driving in Vehicle-Edge-Cloud Networks
  2. 2026Task-oriented Framework for Communication-Efficient Federated Learning: From Isolated Optimization to Holistic Synergy
  3. 2025Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control
  4. 2024Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks
  5. 2022HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation
  6. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design
  7. 2024Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
  8. 2026A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform