# Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks

arXiv: https://arxiv.org/abs/2505.01186 · HTML: https://mlsanity.com/paper/2505.01186/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 2 May 2025.

## 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)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients](https://mlsanity.com/paper/2203.08669/) (2022)
- [FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective](https://mlsanity.com/paper/2110.13864/) (2021)
- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2109.09955/) (2021)
- [Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2401.09656/) (2024)
- [Secure Federated Learning against Model Poisoning Attacks via Client Filtering](https://mlsanity.com/paper/2304.00160/) (2023)
- [Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs](https://mlsanity.com/paper/2401.10361/) (2024)
- [Analytical Models of the Performance of IEEE 802.11p Vehicle to Vehicle Communications](https://mlsanity.com/paper/2104.07923/) (2021)
- [Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems](https://mlsanity.com/paper/2110.09073/) (2021)
- [Defending against Data Poisoning Attacks in Federated Learning via User Elimination](https://mlsanity.com/paper/2404.12778/) (2024)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Multi-Task Federated Learning in VANETs](https://mlsanity.com/paper/2608.08111/) (2026)

## Abstract (arXiv)

Hierarchical Federated Learning (HFL) has recently emerged as a promising solution for intelligent decision-making in vehicular networks, helping to address challenges such as limited communication resources, high vehicle mobility, and data heterogeneity. However, HFL remains vulnerable to adversarial and unreliable vehicles, whose misleading updates can significantly compromise the integrity and convergence of the global model. To address these challenges, we propose a novel defense framework that integrates dynamic vehicle selection with robust anomaly detection within a cluster-based HFL architecture, specifically designed to counter Gaussian noise and gradient ascent attacks. The framework performs a comprehensive reliability assessment for each vehicle by evaluating historical accuracy, contribution frequency, and anomaly records. Anomaly detection combines Z-score and cosine similarity analyses on model updates to identify both statistical outliers and directional deviations in model updates. To further refine detection, an adaptive thresholding mechanism is incorporated into the cosine similarity metric, dynamically adjusting the threshold based on the historical accuracy of each vehicle to enforce stricter standards for consistently high-performing vehicles. In addition, a weighted gradient averaging mechanism is implemented, which assigns higher weights to gradient updates from more trustworthy vehicles. To defend against coordinated attacks, a cross-cluster consistency check is applied to identify collaborative attacks in which multiple compromised clusters coordinate misleading updates. Together, these mechanisms form a multi-level defense strategy to filter out malicious contributions effectively. Simulation results show that the proposed algorithm significantly reduces convergence time compared to benchmark methods across both 1-hop and 3-hop topologies.