  Although federated learning has made awe-inspiring advances, most studies
have assumed that the client's data are fully labeled. However, in a real-world
scenario, every client may have a significant amount of unlabeled instances.
Among the various approaches to utilizing unlabeled data, a federated active
learning framework has emerged as a promising solution. In the decentralized
setting, there are two types of available query selector models, namely
'global' and 'local-only' models, but little literature discusses their
performance dominance and its causes. In this work, we first demonstrate that
the superiority of two selector models depends on the global and local
inter-class diversity. Furthermore, we observe that the global and local-only
models are the keys to resolving the imbalance of each side. Based on our
findings, we propose LoGo, a FAL sampling strategy robust to varying local
heterogeneity levels and global imbalance ratio, that integrates both models by
two steps of active selection scheme. LoGo consistently outperforms six active
learning strategies in the total number of 38 experimental settings.
