Federated Source-Free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data

Junki Mori, Kosuke Kihara, Taiki Miyagawa, Akinori F. Ebihara, Isamu Teranishi, Hisashi Kashima; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 6879-6889

Federated learning (FL) commonly assumes that the server or some clients have labeled data which is often impractical due to annotation costs and privacy concerns. Addressing this problem we focus on a source-free domain adaptation task where (1) the server holds a pre-trained model on labeled source domain data (2) clients possess only unlabeled data from various target domains and (3) the server and clients cannot access the source data in the adaptation phase. This task is known as Federated source-Free Domain Adaptation (FFREEDA). Specifically we focus on classification tasks while the previous work solely studies semantic segmentation. Our contribution is the novel Federated learning with Weighted Cluster Aggregation (FedWCA) method designed to mitigate both domain shifts and privacy concerns with only unlabeled data. Fed-WCA comprises three phases: private and parameter-free clustering of clients to obtain domain-specific global models on the server weighted aggregation of the global models for the clustered clients and local domain adaptation with pseudo-labeling. Experimental results show that Fed-WCA surpasses several existing methods and baselines in FFREEDA establishing its effectiveness and practicality.