提出联合检测与评估机制，实现联邦学习中隐私保护下的行为检测与贡献量化。
Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning
- 融合QI与FedGT优点，兼顾恶意行为检测与贡献评估
- 实验显示在隐私保护下性能优于单一方法
- 适合关注联邦学习安全与公平性的研究者
联邦学习结合安全聚合可在不泄露客户端敏感信息的前提下实现分布式协作学习。然而，安全聚合也增加了恶意客户端行为检测与个体贡献评估的难度。现有方法如QI（Pejo et al.）虽能评估贡献但检测精度不足，因每轮随机选客户端；而FedGT（Xhemrishi et al.）具备行为检测能力却无法评估贡献。本文结合两者的优点，提出同时支持鲁棒行为检测与精准贡献评估的新框架。实验表明，该方法在保持隐私保护前提下，性能显著优于单独使用任一方法。
原文摘要 · Abstract (English)
Federated learning with secure aggregation enables private and collaborative learning from decentralised data without leaking sensitive client information. However, secure aggregation also complicates the detection of malicious client behaviour and the evaluation of individual client contributions to the learning. To address these challenges, QI (Pejo et al.) and FedGT (Xhemrishi et al.) were proposed for contribution evaluation (CE) and misbehaviour detection (MD), respectively. QI, however, lacks adequate MD accuracy due to its reliance on the random selection of clients in each training round, while FedGT lacks the CE ability. In this work, we combine the strengths of QI and FedGT to achieve both robust MD and accurate CE. Our experiments demonstrate superior performance compared to using either method independently.
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