Federated learning (FL), an emerging distributed machine learning paradigm, has been applied to various privacy-preserving scenarios. However, due to its distributed nature, FL faces two key issues: the non-independent and identical distribution (non-IID) of user data and vulnerability to Byzantine threats. To address these challenges, in this paper, we propose FedCAP, a robust FL framework against both data heterogeneity and Byzantine attacks. The core of FedCAP is a model update calibration mechanism to help a server capture the differences in the direction and magnitude of model updates among clients. Furthermore, we design a customized model aggregation rule that facilitates collaborative training among similar clients while accelerating the model deterioration of malicious clients. With a Euclidean norm-based anomaly detection mechanism, the server can quickly identify and permanently remove malicious clients. Moreover, the impact of data heterogeneity and Byzantine attacks can be further mitigated through personalization on the client side. We conduct extensive experiments, comparing multiple state-of-the-art baselines, to demonstrate that FedCAP performs well in several non-IID settings and shows strong robustness under a series of poisoning attacks.

本研究针对联邦学习中的非独立同分布数据和拜占庭威胁这两个关键问题，提出了一种名为FedCAP的稳健框架。其核心是模型更新校准机制，能够帮助服务器捕捉客户端间模型更新的方向和幅度差异，同时设计了定制的模型聚合规则，以促进相似客户端的协作训练并加速恶意客户端的模型退化。实验结果表明，FedCAP在多种非独立同分布设置下表现出色，并在一系列攻击场景中表现出强大的鲁棒性。

FedCAP：通过定制聚合和个性化实现稳健的联邦学习