做联邦学习或电动汽车电池管理的团队，这篇论文给出了一个去中心化且抗攻击的实用框架，值得关注其 FLECA 聚合协议和区块链集成思路。
该论文提出 ABC-DFL，一种自动拜占庭容错的聚类去中心化联邦学习框架，用于联网电动汽车的电池智能管理。它用开放许可区块链替代传统中心化聚合服务器，引入动态 QBFT 共识协议和预言机聚合层，增强信任与安全性。核心算法 FLECA 通过自适应阈值过滤恶意更新，并利用鲁棒聚类隔离可信组，在拜占庭攻击下攻击影响得分低于 0.10。实验表明，FLECA 在良性条件下与 FedProx 收敛性相当，且多任务学习验证了激励机制的有效性与公平性。该工作解决了集中式联邦学习在安全与信任方面的关键限制，为电动汽车电池数据隐私保护提供了新方案。
Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
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.