Fairness and Accuracy in Federated Learning

Understand

In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure privacy.

• Normally, inconsistent distribution of data across different devices in a federated network and limited communication bandwidth between end devices impose both statistical heterogeneity and expensive communication as major challenges for federated learning.
• This paper proposes an algorithm to achieve more fairness and accuracy in federated learning (FedFa).
• It introduces an optimization scheme that employs a double momentum gradient, thereby accelerating the convergence rate of the model.