Using Class and Domain Information to Address Domain Shift in Federated Learning.

Abstract

In federated learning (FL), heterogeneous and client-specific data distributions cause a domain-shift problem, which leads to divergent local models and degraded global performance. To address this problem, this study proposes a class- and domain-aware FL framework that decouples and collaboratively learns the domain-invariant and domain-specific representations. During client training, a novel cross-gated feature separation (CGFS) module is employed to separate the domain features from the class features. A heterogeneous prototype contrastive learning (HPCL) module is then used to guide the learning of the class features and domain features with good discriminability within each feature space. Finally, during server aggregation, a gradient-reweighted hierarchical aggregation (GHA) strategy is applied to effectively aggregate information from all the clients and build a global model with good robustness to domain variation. The experimental results obtained on two FL datasets with domain shift show that the proposed method consistently outperforms state-of-the-art approaches.