FedIBD: a federated learning framework in asynchronous mode for imbalanced data

With the development of edge computing and Internet of Things (IoT), the computing power of edge devices continues to increase, and the data obtained is more specific and private. Methods based on Federated Learning (FL) can help utilize the data that exists widely on edge devices in a privacy-preserving way and train a shareable global model collaboratively. However, the imbalanced data from edge devices pose a huge challenge to FL, as data features extracted from uneven, biased, and incomplete samples complicate the model aggregation process required to achieve well-performing models. To support FL on imbalanced data, a new asynchronous FL framework, named FedIBD: Federated learning framework in Asynchronous mode for Imbalanced Data, is proposed. FedIBD not only considers the temporal inconsistency in asynchronous learning but also measures the informative differences in imbalanced data to support FL in asynchronous and heterogeneous environments. Compared with the existing synchronous and asynchronous FL methods, FedIBD can achieve significantly better performance in terms of accuracy, communication time and cost on imbalanced data.