Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation

TL;DR

This paper introduces SemiAnAgg, a novel federated semi-supervised learning method that effectively aggregates unlabeled client models using an anchor model, significantly improving performance on multiple benchmarks.

Contribution

The paper proposes SemiAnAgg, a new anchor-based aggregation method that leverages an anchor model to assess unlabeled client contributions, addressing client drift and heterogeneity issues.

Findings

Achieves 9% accuracy improvement on CIFAR-100

Improves recall by 7.6% on ISIC-18

Outperforms prior state-of-the-art methods on four benchmarks

Abstract

Federated semi-supervised learning (FedSemi) refers to scenarios where there may be clients with fully labeled data, clients with partially labeled, and even fully unlabeled clients while preserving data privacy. However, challenges arise from client drift due to undefined heterogeneous class distributions and erroneous pseudo-labels. Existing FedSemi methods typically fail to aggregate models from unlabeled clients due to their inherent unreliability, thus overlooking unique information from their heterogeneous data distribution, leading to sub-optimal results. In this paper, we enable unlabeled client aggregation through SemiAnAgg, a novel Semi-supervised Anchor-Based federated Aggregation. SemiAnAgg learns unlabeled client contributions via an anchor model, effectively harnessing their informative value. Our key idea is that by feeding local client data to the same global model and the same consistently initialized anchor model (i.e., random model), we can measure the importance of each unlabeled client accordingly. Extensive experiments demonstrate that SemiAnAgg achieves new state-of-the-art results on four widely used FedSemi benchmarks, leading to substantial performance improvements: a 9% increase in accuracy on CIFAR-100 and a 7.6% improvement in recall on the medical dataset ISIC-18, compared with prior state-of-the-art. Code is available at: https://github.com/xmed-lab/SemiAnAgg.