  Federated learning aims to share private data to maximize the data utility
without privacy leakage. Previous federated learning research mainly focuses on
multi-class classification problems. However, multi-label classification is a
crucial research problem close to real-world data properties. Nevertheless, a
limited number of federated learning studies explore this research problem.
Existing studies of multi-label federated learning did not consider the
characteristics of multi-label data, i.e., they used the concept of multi-class
classification to verify their methods' performance, which means it will not be
feasible to apply their methods to real-world applications. Therefore, this
study proposed a new multi-label federated learning framework with a
Clustering-based Multi-label Data Allocation (CMDA) and a novel aggregation
method, Fast Label-Adaptive Aggregation (FLAG), for multi-label classification
in the federated learning environment. The experimental results demonstrate
that our methods only need less than 50\% of training epochs and communication
rounds to surpass the performance of state-of-the-art federated learning
methods.
