FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models

• URL: http://arxiv.org/abs/2403.09904v1
• Date: Thu, 14 Mar 2024 22:29:59 GMT
• Title: FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
• Authors: Kai Yi, Georg Meinhardt, Laurent Condat, Peter Richtárik,
• Abstract summary: Federated Learning (FL) has garnered increasing attention due to its unique characteristic of allowing heterogeneous clients to process their private data locally and interact with a central server. We introduce FedComLoc, integrating practical and effective compression into emphScaffnew to further enhance communication efficiency.
• Score: 56.21666819468249
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) has garnered increasing attention due to its unique characteristic of allowing heterogeneous clients to process their private data locally and interact with a central server, while being respectful of privacy. A critical bottleneck in FL is the communication cost. A pivotal strategy to mitigate this burden is \emph{Local Training}, which involves running multiple local stochastic gradient descent iterations between communication phases. Our work is inspired by the innovative \emph{Scaffnew} algorithm, which has considerably advanced the reduction of communication complexity in FL. We introduce FedComLoc (Federated Compressed and Local Training), integrating practical and effective compression into \emph{Scaffnew} to further enhance communication efficiency. Extensive experiments, using the popular TopK compressor and quantization, demonstrate its prowess in substantially reducing communication overheads in heterogeneous settings.

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