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Federated learning aims to share private data to maximize the data utilitywithout privacy leakage. Previous federated learning research mainly focuses onmulti-class classification problems. However, multi-label classification is acrucial research problem close to real-world data properties. Nevertheless, alimited number of federated learning studies explore this research problem.Existing studies of multi-label federated learning did not consider thecharacteristics of multi-label data, i.e., they used the concept of multi-classclassification to verify their methods' performance, which means it will not befeasible to apply their methods to real-world applications. Therefore, thisstudy proposed a new multi-label federated learning framework with aClustering-based Multi-label Data Allocation (CMDA) and a novel aggregationmethod, Fast Label-Adaptive Aggregation (FLAG), for multi-label classificationin the federated learning environment. The experimental results demonstratethat our methods only need less than 50\% of training epochs and communicationrounds to surpass the performance of state-of-the-art federated learningmethods.

FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning