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.

翻译：联邦学习（FL）已成为管理智能交通系统（ITS）中电动汽车（EV）电池数据的一种有前景的范式，支持异常检测和容量估计等隐私保护任务。然而，大多数现有框架依赖集中式聚合方案，这在安全性和信任方面存在关键局限性。为解决这些挑战，我们提出了ABC-DFL——一种面向联网电动汽车的自动化拜占庭容错集群去中心化联邦学习（C-DFL）框架。所提出的激励驱动型C-DFL系统用开放许可区块链替代中心服务器，并采用新的动态仲裁拜占庭容错（QBFT）协议和基于预言机的聚合层，以增强信任、安全性和自动化。ABC-DFL的核心是FLECA（过滤分层增强型聚类聚合），这是一种鲁棒的层次化聚合协议，通过让每辆电动汽车基于其参考模型更新的偏差自适应阈值过滤恶意更新，从而缓解拜占庭攻击。负责组间聚合的预言机节点采用鲁棒聚类技术，从可信的电动汽车组中隔离并聚合模型更新。全面的实验评估表明，FLECA在良性条件下与FedProx收敛性相匹配，并在自适应对抗场景下以低于0.10的攻击影响得分显著优于现有防御方法。此外，针对多任务模型的多项学习实验验证了激励机制的效力和公平性。最后，链上和链下基准测试证实了ABC-DFL的实用性。