Universal Federated Domain Adaptation through One-vs-All Self-Supervision for Internet of Things

In practical Internet of Things (IoT) applications, deep neural networks (DNNs) often encounter challenges arising from covariate shifts (differences in feature distributions) and category shifts (discrepancies in label spaces), which significantly degrade their generalization performance. To mitigate these issues, universal federated domain adaptation (UFDA) techniques have been proposed to train a global model that can classify known and unknown categories while keeping data private. Nevertheless, most existing methods still struggle to precisely identify samples belonging to unknown classes in the target domain due to the unavailability of data from the source domain clients. To address these challenges, we propose a novel method, termed one-vs-all self-supervision (OSS) for IoT scenario. Specifically, OSS mainly consists of following three components. First, one-vs-all pseudo-label generation is proposed to generate high-quality pseudo-labels by leveraging source client models. Subsequently, we design a category-diverse strategy to aggregate the source models by assigning appropriate weights to each source domain client. Finally, we implement a target self-supervised learning strategy to refine feature alignment with respect to cluster centers. Comprehensive experiments are performed on four benchmark datasets: Office-31, Office-Home, VisDA-2017+ImageCLEF-DA, and Digits. The results show that our proposed OSS method achieves state-of-the-art performance in UFDA, significantly enhancing the recognition accuracy.