Rayana Jaafar

Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients

We consider a standard federated learning (FL) setup where a group of clients periodically coordinate with a central server to train a statistical model. We develop a general algorithmic framework called FedLin to tackle some of the key challenges intrinsic to FL, namely objective heterogeneity, sys…