Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks

2025-05-02

AI Summary

In hierarchical federated learning (HFL) for vehicular networks, malicious vehicles can launch Gaussian noise injection and gradient ascent attacks, degrading model integrity and convergence. To address this, we propose a cluster-based secure HFL framework. Our key contributions are: (1) a novel dual-mode anomaly detection mechanism integrating Z-score statistical analysis with adaptive-threshold cosine similarity; (2) cross-cluster consistency verification to detect coordinated attacks; and (3) a historical-trustworthiness-based weighted gradient aggregation strategy. Experiments under one-hop and three-hop network topologies demonstrate that the framework significantly accelerates model convergence, effectively filters malicious updates, and enhances both convergence stability and robustness—while preserving global model integrity.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Adversarial Attacks & Robustness
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Secures HFL in vehicular networks against adversarial vehicles
• Detects anomalies using dynamic thresholds and reliability metrics
• Defends against coordinated attacks via cross-cluster consistency checks

Innovation

• Dynamic vehicle selection with robust anomaly detection
• Adaptive thresholding for cosine similarity analysis
• Weighted gradient averaging for trustworthy updates