mlsanityData to 14 Oct 2026

Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs

arXiv 2401.10361 ↗First posted 18 Jan 2024cs.LG cs.AI cs.DCcited by 7

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Abstract (arXiv)

The usage of federated learning (FL) in Vehicular Ad hoc Networks (VANET) has garnered significant interest in research due to the advantages of reducing transmission overhead and protecting user privacy by communicating local dataset gradients instead of raw data. However, implementing FL in VANETs faces challenges, including limited communication resources, high vehicle mobility, and the statistical diversity of data distributions. In order to tackle these issues, this paper introduces a novel framework for hierarchical federated learning (HFL) over multi-hop clustering-based VANET. The proposed method utilizes a weighted combination of the average relative speed and cosine similarity of FL model parameters as a clustering metric to consider both data diversity and high vehicle mobility. This metric ensures convergence with minimum changes in cluster heads while tackling the complexities associated with non-independent and identically distributed (non-IID) data scenarios. Additionally, the framework includes a novel mechanism to manage seamless transitions of cluster heads (CHs), followed by transferring the most recent FL model parameter to the designated CH. Furthermore, the proposed approach considers the option of merging CHs, aiming to reduce their count and, consequently, mitigate associated overhead. Through extensive simulations, the proposed hierarchical federated learning over clustered VANET has been demonstrated to improve accuracy and convergence time significantly while maintaining an acceptable level of packet overhead compared to previously proposed clustering algorithms and non-clustered VANET.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 16 of its 45 references that are in the map. First posted on arXiv 18 Jan 2024.

Builds on

16 of 45 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  4. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  5. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  6. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  7. 2020Federated Learning in Vehicular Networkscited by 53
  8. 2020Federated Edge Learning : Design Issues and Challengescited by 26
  9. 20206G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunitiescited by 24
  10. 2022Clustered Vehicular Federated Learning: Process and Optimizationcited by 15
  11. 2023Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescuecited by 14
  12. 2021Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systemscited by 10
4 more
  1. 2022FL_PyTorch: optimization research simulator for federated learningcited by 10
  2. 2021Analytical Models of the Performance of IEEE 802.11p Vehicle to Vehicle Communicationscited by 6
  3. 2022Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sourcescited by 5
  4. 2016Multi-Hop Cluster based IEEE 802.11p and LTE Hybrid Architecture for VANET Safety Message Disseminationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 5.

7 papers.

  1. 2025Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networkscited by 1
  2. 2024Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detectioncited by 0
  3. 2026Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizerscited by 0
  4. 2026SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning Systemcited by 0
  5. 2026Hierarchical Multi-Task Federated Learning in VANETscited by 0
  6. 2026Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networkscited by 0
  7. 2026Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETscited by 0