  While existing federated learning approaches mostly require that clients have
fully-labeled data to train on, in realistic settings, data obtained at the
client-side often comes without any accompanying labels. Such deficiency of
labels may result from either high labeling cost, or difficulty of annotation
due to the requirement of expert knowledge. Thus the private data at each
client may be either partly labeled, or completely unlabeled with labeled data
being available only at the server, which leads us to a new practical federated
learning problem, namely Federated Semi-Supervised Learning (FSSL). In this
work, we study two essential scenarios of FSSL based on the location of the
labeled data. The first scenario considers a conventional case where clients
have both labeled and unlabeled data (labels-at-client), and the second
scenario considers a more challenging case, where the labeled data is only
available at the server (labels-at-server). We then propose a novel method to
tackle the problems, which we refer to as Federated Matching (FedMatch).
FedMatch improves upon naive combinations of federated learning and
semi-supervised learning approaches with a new inter-client consistency loss
and decomposition of the parameters for disjoint learning on labeled and
unlabeled data. Through extensive experimental validation of our method in the
two different scenarios, we show that our method outperforms both local
semi-supervised learning and baselines which naively combine federated learning
with semi-supervised learning. The code is available at
https://github.com/wyjeong/FedMatch.

Machine Learning;Machine Learning