FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction

Understand

Federated Learning (FL) is a way for machines to learn from data that is kept locally, in order to protect the privacy of clients.

• This is typically done using local SGD, which helps to improve communication efficiency.
• However, such a scheme is currently constrained by slow and unstable convergence due to the variety of data on different clients' devices.
• In this work, we identify three under-explored phenomena of biased local learning that may explain these challenges caused by local updates in supervised FL.