# Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs

arXiv: https://arxiv.org/abs/2401.10361 · HTML: https://mlsanity.com/paper/2401.10361/

## 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 (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2006.01412/) (2020)
- [Federated Edge Learning : Design Issues and Challenges](https://mlsanity.com/paper/2009.00081/) (2020)
- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [Clustered Vehicular Federated Learning: Process and Optimization](https://mlsanity.com/paper/2201.11271/) (2022)
- [Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue](https://mlsanity.com/paper/2309.16709/) (2023)
- [Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems](https://mlsanity.com/paper/2102.10142/) (2021)
- [FL_PyTorch: optimization research simulator for federated learning](https://mlsanity.com/paper/2202.03099/) (2022)
- [Analytical Models of the Performance of IEEE 802.11p Vehicle to Vehicle Communications](https://mlsanity.com/paper/2104.07923/) (2021)
- [Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources](https://mlsanity.com/paper/2209.01750/) (2022)
- [Multi-Hop Cluster based IEEE 802.11p and LTE Hybrid Architecture for VANET Safety Message Dissemination](https://mlsanity.com/paper/1603.05628/) (2016)

## Built on by (most cited first, 7 of 7)

- [Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2505.01186/) (2025)
- [Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection](https://mlsanity.com/paper/2405.17497/) (2024)
- [Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers](https://mlsanity.com/paper/2606.06687/) (2026)
- [SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System](https://mlsanity.com/paper/2606.18384/) (2026)
- [Hierarchical Multi-Task Federated Learning in VANETs](https://mlsanity.com/paper/2608.08111/) (2026)
- [Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks](https://mlsanity.com/paper/2608.11532/) (2026)
- [Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETs](https://mlsanity.com/paper/2609.36157/) (2026)

## 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.