针对联邦学习中的概念漂移问题，提出分类器聚类与特征对齐的新框架。
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift
- 按类别聚类本地分类器，生成特征锚点以促进协作。
- 在多种概念漂移场景下，性能显著优于现有方法。
- 适合数据分布差异大且存在漂移的分布式学习任务。
数据异构是联邦学习的关键挑战之一，而客户端因不同概念漂移导致的分布变化（即分布式概念漂移）尚未被充分研究。本文聚焦真实漂移，即条件分布 $P(Y|X)$ 的变化。研究发现，本地分类器在漂移适应中起关键作用；同时，在分布式概念漂移下，特征对齐受 $P(Y|X)$ 和数据异构程度影响显著。为此，提出 FedCCFA 框架：通过类别级分类器聚类生成特征锚点，并基于标签分布熵 $P(Y)$ 自适应对齐各客户端特征空间，缓解特征空间不一致。实验表明，该方法在多种概念漂移设置下均显著优于现有方法。代码已开源。
原文摘要 · Abstract (English)
Data heterogeneity is one of the key challenges in federated learning, and many efforts have been devoted to tackling this problem. However, distributed concept drift with data heterogeneity, where clients may additionally experience different concept drifts, is a largely unexplored area. In this work, we focus on real drift, where the conditional distribution $P(Y|X)$ changes. We first study how distributed concept drift affects the model training and find that local classifier plays a critical role in drift adaptation. Moreover, to address data heterogeneity, we study the feature alignment under distributed concept drift, and find two factors that are crucial for feature alignment: the conditional distribution $P(Y|X)$ and the degree of data heterogeneity. Motivated by the above findings, we propose FedCCFA, a federated learning framework with classifier clustering and feature alignment. To enhance collaboration under distributed concept drift, FedCCFA clusters local classifiers at class-level and generates clustered feature anchors according to the clustering results. Assisted by these anchors, FedCCFA adaptively aligns clients' feature spaces based on the entropy of label distribution $P(Y)$, alleviating the inconsistency in feature space. Our results demonstrate that FedCCFA significantly outperforms existing methods under various concept drift settings. Code is available at https://github.com/Chen-Junbao/FedCCFA.
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