Federated Learning with Only Positive Labels by Exploring Label Correlations

arXiv:2404.15598v1 Announce Type: new
Abstract: 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 …

abstract arxiv classification constraints correlations cs.cr cs.lg data federated learning labels learn multiple paper performance positive privacy solution study type