  Federated learning aims to collaboratively learn a model by using the data
from multiple users under privacy constraints. In this paper, we study the
multi-label classification problem under the federated learning setting, where
trivial solution and extremely poor performance may be obtained, especially
when only positive data w.r.t. a single class label are provided for each
client. This issue can be addressed by adding a specially designed regularizer
on the server-side. Although effective sometimes, the label correlations are
simply ignored and thus sub-optimal performance may be obtained. Besides, it is
expensive and unsafe to exchange user's private embeddings between server and
clients frequently, especially when training model in the contrastive way. To
remedy these drawbacks, we propose a novel and generic method termed Federated
Averaging by exploring Label Correlations (FedALC). Specifically, FedALC
estimates the label correlations in the class embedding learning for different
label pairs and utilizes it to improve the model training. To further improve
the safety and also reduce the communication overhead, we propose a variant to
learn fixed class embedding for each client, so that the server and clients
only need to exchange class embeddings once. Extensive experiments on multiple
popular datasets demonstrate that our FedALC can significantly outperform
existing counterparts.
