mlsanityData to 7 Oct 2026

Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks

arXiv 2401.09656 ↗First posted 18 Jan 2024cs.LG cs.AI cs.DCcited by 11

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Abstract (arXiv)

Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-preserving manner. In this paper, we consider HFL with highly mobile devices, mainly targeting at vehicular networks. Through convergence analysis, we show that mobility influences the convergence speed by both fusing the edge data and shuffling the edge models. While mobility is usually considered as a challenge from the perspective of communication, we prove that it increases the convergence speed of HFL with edge-level heterogeneous data, since more diverse data can be incorporated. Furthermore, we demonstrate that a higher speed leads to faster convergence, since it accelerates the fusion of data. Simulation results show that mobility increases the model accuracy of HFL by up to 15.1% when training a convolutional neural network on the CIFAR-10 dataset.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 33 of its 59 references that are in the map. First posted on arXiv 18 Jan 2024.

Builds on

33 of 59 references.

References · 33

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  6. 2018Federated Learning with Non-IID Datacited by 1,099
  7. 2020Adaptive Federated Optimizationcited by 1,036
  8. 2017Federated Multi-Task Learningcited by 669
  9. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  10. 2019Federated Learning with Personalization Layerscited by 533
  11. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  12. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
21 more
  1. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  2. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  3. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  4. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  5. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  6. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  7. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
  8. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  9. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  10. 2020Federated Learning in Vehicular Networkscited by 53
  11. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  12. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  13. 2023Why Batch Normalization Damage Federated Learning on Non-IID Data?cited by 29
  14. 2020Communicate to Learn at the Edgecited by 27
  15. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  16. 2022Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Datacited by 18
  17. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  18. 2018Toward Intelligent Vehicular Networks: A Machine Learning Frameworkcited by 11
  19. 2020Edge Learning with Timeliness Constraints: Challenges and Solutionscited by 10
  20. 2022MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networkscited by 5
  21. 2023Data-Heterogeneous Hierarchical Federated Learning with Mobilitycited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 7; 2026: 1.

11 papers.

  1. 2024Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learningcited by 3
  2. 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
  3. 2025Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoTcited by 2
  4. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  5. 2025Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networkscited by 1
  6. 2025FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learningcited by 1
  7. 2026C2FL: Clustered Continual Federated Learning under Spatial and Temporal Driftcited by 1
  8. 2024Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detectioncited by 0
  9. 2025HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learningcited by 0
  10. 2025Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsificationcited by 0
  11. 2025Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approachcited by 0