Resource-Efficient Federated Learning

• URL: http://arxiv.org/abs/2111.01108v1
• Date: Mon, 1 Nov 2021 17:21:07 GMT
• Title: Resource-Efficient Federated Learning
• Authors: Ahmed M. Abdelmoniem and Atal Narayan Sahu and Marco Canini and Suhaib A. Fahmy
• Abstract summary: Federated Learning (FL) enables distributed training by learners using local data. It presents numerous challenges relating to the data distribution, device capabilities, and participant availability as scale deployments.
• Score: 3.654036881216688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges relating to the heterogeneity of the data distribution, device capabilities, and participant availability as deployments scale, which can impact both model convergence and bias. Existing FL schemes use random participant selection to improve fairness; however, this can result in inefficient use of resources and lower quality training. In this work, we systematically address the question of resource efficiency in FL, showing the benefits of intelligent participant selection, and incorporation of updates from straggling participants. We demonstrate how these factors enable resource efficiency while also improving trained model quality.

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