  Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm for
allowing distributed clients to collaboratively train a machine learning model
over scarce labeled data and abundant unlabeled data. However, existing works
for FedSSL rely on a closed-world assumption that all local training data and
global testing data are from seen classes observed in the labeled dataset. It
is crucial to go one step further: adapting FL models to an open-world setting,
where unseen classes exist in the unlabeled data. In this paper, we propose a
novel Federatedopen-world Semi-Supervised Learning (FedoSSL) framework, which
can solve the key challenge in distributed and open-world settings, i.e., the
biased training process for heterogeneously distributed unseen classes.
Specifically, since the advent of a certain unseen class depends on a client
basis, the locally unseen classes (exist in multiple clients) are likely to
receive differentiated superior aggregation effects than the globally unseen
classes (exist only in one client). We adopt an uncertainty-aware suppressed
loss to alleviate the biased training between locally unseen and globally
unseen classes. Besides, we enable a calibration module supplementary to the
global aggregation to avoid potential conflicting knowledge transfer caused by
inconsistent data distribution among different clients. The proposed FedoSSL
can be easily adapted to state-of-the-art FL methods, which is also validated
via extensive experiments on benchmarks and real-world datasets (CIFAR-10,
CIFAR-100 and CINIC-10).
