Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs

2026-05-20

AI Summary

This work addresses the vulnerability of centralized federated learning in vehicular networks to Byzantine attacks and its inherent security and trust bottlenecks by proposing ABC-DFL, a decentralized federated learning framework. ABC-DFL replaces the central server with a permissioned blockchain and integrates a dynamic Quorum BFT consensus mechanism with an oracle-driven cross-group aggregation scheme. It further introduces the FLECA hierarchical aggregation protocol and an incentive-based trustworthy grouping strategy, uniquely combining adaptive threshold filtering with robust clustering to defend against adaptive Byzantine attacks. Experimental results demonstrate that ABC-DFL achieves convergence performance comparable to FedProx under benign conditions while maintaining an attack impact score below 0.10 in adversarial scenarios, thereby offering a secure, fair, and practically deployable solution.

Abstract

Federated learning (FL) has emerged as a promising paradigm for managing electric vehicle (EV) battery data in intelligent transportation systems (ITS), enabling privacy-preserving tasks such as anomaly detection and capacity estimation. However, most existing frameworks rely on centralized aggregation schemes, which pose critical limitations in terms of security and trust. To address these challenges, we propose ABC-DFL, an automated Byzantine-resilient clustered decentralized federated learning (C-DFL) framework for connected EVs. The proposed incentive-driven C-DFL system replaces the central server with an open-permissioned blockchain, featuring a new dynamic Quorum Byzantine Fault Tolerance (QBFT) protocol and an oracle-based aggregation layer, to enhance trust, security, and automation. At the core of ABC-DFL lies FLECA (Filtered Layered Enhanced Clustering Aggregation), a robust hierarchical aggregation protocol that mitigates Byzantine attacks by having each EV filter malicious updates using an adaptive threshold based on deviations from its reference model update. Oracle nodes, responsible for inter-group aggregation, employ robust clustering to isolate and aggregate model updates from trustworthy EV groups. Comprehensive experimental evaluations demonstrate that FLECA matches FedProx convergence under benign conditions and significantly outperforms existing defenses with attack impact scores below 0.10 in adaptive adversarial scenarios. Furthermore, several learning experiments with multitask models confirm the effectiveness and fairness of the incentive mechanism. Finally, on-chain and off-chain benchmarks validate the practicality of ABC-DFL.

Problem

• Federated Learning
• Byzantine Resilience
• Decentralized Learning
• Electric Vehicles
• Battery Intelligence

Innovation

• Byzantine-resilient
• decentralized federated learning
• blockchain
• FLECA
• dynamic QBFT