Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning Sen Cui 1 Weishen Pan

Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples across different sources.