Federated Single-positive Multi-label Learning

Single-positive multi-label learning (SPMLL) aims to train a multi-label classifier from data with single-positive label, to predict all applicable labels during testing. However, existing SPMLL methods are tailored for centralized datasets, which fail to be directly deployed to distributed setting like federated learning. In this paper, we start the first attempt to study federated single-positive multi-label learning (FedSPMLL), aiming to collaboratively train a SPMLL model from distributed data. To achieve this, we need to address challenges caused by label incompleteness: limited generalization ability of local model and overweighting contribution of client with local dataset suffering from severe label incompleteness. To this end, we propose a novel FedLOG method, guiding FedSPMLL with predicate LOGic-modeled label correlation. Enabling the informative knowledge extraction from limited data, we propose to model label correlation within local dataset using predicate logic. To alleviate false negative label issue, we propose to transfer confident label correlation knowledge to local model by self-distillation. To downweight the contribution of unreliable client owning dataset with severe label incompleteness, we propose a new measurement of label incompleteness to adjust client contribution for a fair aggregation. We establish a comprehensive FedSPMLL benchmark. And extensive experiments demonstrate the superiority of our FedLOG method.