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Eco-Fedsplit: Federated Learning with Error-Compensated Compression | IEEE Conference Publication | IEEE Xplore

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 lea...Show More

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.
Date of Conference: 23-27 May 2022
Date Added to IEEE Xplore: 27 April 2022
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Conference Location: Singapore, Singapore

References

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