摘要
Federated learning(FL) is a machine learning paradigm for data silos and privacy protection,which aims to organize multiple clients for training global machine learning models without exposing data to all parties.However,when dealing with non-independently identically distributed(non-ⅡD) client data,FL cannot obtain more satisfactory results than centrally trained machine learning and even fails to match the accuracy of the local model obtained by client training alone.To analyze and address the above issues,we survey the state-of-theart methods in the literature related to FL on non-ⅡD data.On this basis,a motivation-based taxonomy,which classifies these methods into two categories,including heterogeneity reducing strategies and adaptability enhancing strategies,is proposed.Moreover,the core ideas and main challenges of these methods are analyzed.Finally,we envision several promising research directions that have not been thoroughly studied,in hope of promoting research in related fields to a certain extent.
