Eco-Fedsplit: Federated Learning with Error-Compensated Compression

Abstract

Federated learning is an emerging framework for collaborative machine-learning on devices which do not want to share local data. State-of-the art methods in federated learning reduce the communication frequency, but are not guaranteed to converge to the optimal model parameters. These methods also experience a communication bottleneck, especially when the devices are power-constrained and communicate over a shared medium. This paper presents ECO-FedSplit, an algorithm that increases the communication efficiency of federated learning without sacrificing solution accuracy. The key is to compress inter-device communication and to compensate for information losses in a theoretically justified manner. We prove strong convergence properties of ECO-FedSplit on strongly convex optimization problems and show that the algorithm yields a highly accurate solution with dramatically reduced communication. Extensive numerical experiments validate our theoretical result on real data sets.