Abstract: Multi-label classification is a fundamental task that requires predicting all applicable labels for each sample. Previous methods often rely heavily on training models with large-scale multi ...

Abstract: Federated learning is an emerging machine learning paradigm that effectively alleviates the data silo problem by distributing the model training process to multiple data holders. However, ...