针对联邦学习中数据漂移导致的聚类失效问题，提出高效自适应聚类方法。
Federated Learning Clients Clustering with Adaptation to Data Drifts
- 根据客户端数据漂移动态检测并选择性重聚类，保持聚类质量
- 相比现有方法提升准确率1.9%-5.9%，加速达1.16倍至2.23倍
- 适用于存在数据漂移、设备异构或恶意节点的现实联邦学习场景
联邦学习（FL）在不集中原始数据的前提下于边缘设备上训练深度模型，保护用户隐私。然而，客户端异构性会减缓收敛速度并限制全局模型精度。聚类联邦学习（CFL）通过将具有相似表示的客户端分组，并为每个簇训练独立模型来缓解此问题。实际中，客户端数据随时间演变，称为数据漂移，破坏了簇内同质性并降低性能。数据漂移可表现为输出值、输入特征或其关系的变化。本文提出FIELDING，一种低开销的CFL框架，能应对多种类型的数据漂移。FIELDING在单个客户端上检测漂移，并执行选择性重聚类，在保持簇质量与模型性能之间取得平衡，同时对恶意客户端和不同异构水平具有鲁棒性。实验表明，FIELDING比现有最先进方法提升最终模型准确率1.9%-5.9%，达成目标精度的速度快1.16-2.23倍。
原文摘要 · Abstract (English)
Federated Learning (FL) trains deep models across edge devices without centralizing raw data, preserving user privacy. However, client heterogeneity slows down convergence and limits global model accuracy. Clustered FL (CFL) mitigates this by grouping clients with similar representations and training a separate model for each cluster. In practice, client data evolves over time, a phenomenon we refer to as data drift, which breaks cluster homogeneity and degrades performance. Data drift can take different forms depending on whether changes occur in the output values, the input features, or the relationship between them. We propose FIELDING, a CFL framework for handling diverse types of data drift with low overhead. FIELDING detects drift at individual clients and performs selective re-clustering to balance cluster quality and model performance, while remaining robust to malicious clients and varying levels of heterogeneity. Experiments show that FIELDING improves final model accuracy by 1.9-5.9% and achieves target accuracy 1.16x-2.23x faster than existing state-of-the-art CFL methods.
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