Data-Heterogeneity

Federated learning is a distributed learning framework that is communication efficient and provides protection over participating users’ raw training data. One outstanding challenge of federate learning comes from the users’ heterogeneity, and …

Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data. User heterogeneity has imposed significant challenges to FL, which …