Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Abstract
Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of the data across different clients which gives rise to the client drift phenomenon. In this work, we propose a general algorithmic framework, \mime, which i) mitigates client drift and ii) adapts arbitrary centralized
optimization algorithms such as SGD and Adam to the federated learning setting. Mime uses a combination of control-variates and server-level statistics (e.g. momentum) at every client-update step to ensure that each local update mimics that of the centralized method run on iid data. We prove a reduction result showing that \mime can translate the convergence of a generic algorithm in the centralized setting into convergence in the federated setting. Further, we show for the first time that multiple local steps can lead to faster convergence in the cross-device FL setting. Our thorough theoretical and empirical analyses establish Mime's superiority over other other baselines.
optimization algorithms such as SGD and Adam to the federated learning setting. Mime uses a combination of control-variates and server-level statistics (e.g. momentum) at every client-update step to ensure that each local update mimics that of the centralized method run on iid data. We prove a reduction result showing that \mime can translate the convergence of a generic algorithm in the centralized setting into convergence in the federated setting. Further, we show for the first time that multiple local steps can lead to faster convergence in the cross-device FL setting. Our thorough theoretical and empirical analyses establish Mime's superiority over other other baselines.
Research Areas
