Federated Learning (FL) enables deep learning model training across edge devices and protects user privacy by retaining raw data locally. Data heterogeneity in client distributions slows model convergence and leads to plateauing with reduced precision. Clustered FL solutions address this by grouping clients with statistically similar data and training models for each cluster. However, maintaining consistent client similarity within each group becomes challenging when data drifts occur, significantly impacting model accuracy. In this paper, we introduce Fielding, a clustered FL framework that handles data drifts promptly with low overheads. Fielding detects drifts on all clients and performs selective label distribution-based re-clustering to balance cluster optimality and model performance, remaining robust to malicious clients and varied heterogeneity degrees. Our evaluations show that Fielding improves model final accuracy by 1.9%-5.9% and reaches target accuracies 1.16x-2.61x faster.

翻译：联邦学习（FL）使得深度学习模型能够在边缘设备上进行训练，并通过将原始数据保留在本地来保护用户隐私。客户端分布中的数据异质性会减缓模型收敛速度，导致模型性能停滞且精度下降。聚类联邦学习解决方案通过将具有统计相似数据的客户端分组并为每个聚类训练模型来解决这一问题。然而，当发生数据漂移时，维持每个组内客户端相似性的一致性变得具有挑战性，这会显著影响模型精度。本文提出Fielding，一种能够以低开销及时处理数据漂移的聚类联邦学习框架。Fielding检测所有客户端上的数据漂移，并执行基于选择性标签分布的重聚类，以平衡聚类最优性与模型性能，同时对恶意客户端和不同程度的异质性保持鲁棒性。我们的评估表明，Fielding将模型的最终精度提高了1.9%-5.9%，并以1.16倍至2.61倍的速度达到目标精度。