  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.
