Title: FedOpenMatch: Towards Semi-Supervised Federated Learning in Open-Set Environments
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: semi-supervised federated learning, open-set, federated learning

Abstract: Semi-supervised federated learning (SSFL) has emerged as an effective approach to leverage unlabeled data distributed across multiple data owners for improving model generalization. Existing SSFL methods typically assume that labeled and unlabeled data share the same label space. However, in realistic federated scenarios, unlabeled data often contain categories absent from the labeled set, i.e., outliers, which can severely degrade the performance of SSFL algorithms.
In this paper, we address this under-explored issue, formally propose the open-set semi-supervised federated learning (OSSFL) problem,  and develop the first OSSFL framework, FedOpenMatch. Our method adopts a one-vs-all (OVA) classifier as the outlier detector, equipped with logit adjustment to mitigate inlier-outlier imbalance and a gradient stop mechanism to reduce feature interference between the OVA and inlier classifiers. In addition, we introduce the logit consistency regularization loss, yielding more robust performance.
Extensive experiments on standard benchmarks across diverse data settings demonstrate the effectiveness of FedOpenMatch, which significantly outperforms the baselines.

TL;DR: We propose the first approach, FedOpenMatch, for semi-supervised federated learning with open-set unlabeled client data, which achieves sota performance under both close-set and open-set tests.