mlsanityData to 7 Oct 2026

Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems

arXiv 2110.09073 ↗First posted 18 Oct 2021cs.LGcited by 6

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Abstract (arXiv)

Cooperative Intelligent Transport System (C-ITS) is a promising network to provide safety, efficiency, sustainability, and comfortable services for automated vehicles and road infrastructures by taking advantages from participants. However, the components of C-ITS usually generate large amounts of data, which makes it difficult to explore data science. Currently, federated learning has been proposed as an appealing approach to allow users to cooperatively reap the benefits from trained participants. Therefore, in this paper, we propose a novel Semi-asynchronous Hierarchical Federated Learning (SHFL) framework for C-ITS that enables elastic edge to cloud model aggregation from data sensing. We further formulate a joint edge node association and resource allocation problem under the proposed SHFL framework to prevent personalities of heterogeneous road vehicles and achieve communication-efficiency. To deal with our proposed Mixed integer nonlinear programming (MINLP) problem, we introduce a distributed Alternating Direction Method of Multipliers (ADMM)-Block Coordinate Update (BCU) algorithm. With this algorithm, a tradeoff between training accuracy and transmission latency has been derived. Numerical results demonstrate the advantages of the proposed algorithm in terms of training overhead and model performance.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 18 Oct 2021.

Builds on

14 of 34 references.

References · 14

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2018Federated Learning with Non-IID Datacited by 1,099
  4. 2017Federated Multi-Task Learningcited by 669
  5. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  7. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  8. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  9. 2019Asynchronous Federated Optimizationcited by 264
  10. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  11. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  12. 2020Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Datacited by 125
2 more
  1. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  2. 2019Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Datacited by 39

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Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 1.

6 papers.

  1. 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
  2. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
  3. 2023Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networkscited by 7
  4. 2023Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysiscited by 4
  5. 2025Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networkscited by 1
  6. 2024Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detectioncited by 0