A Contribution-based Device Selection Scheme in Federated Learning

• URL: http://arxiv.org/abs/2203.05369v1
• Date: Thu, 10 Mar 2022 13:40:48 GMT
• Title: A Contribution-based Device Selection Scheme in Federated Learning
• Authors: Shashi Raj Pandey, Lam D. Nguyen, and Petar Popovski
• Abstract summary: In a Federated Learning (FL) setup, a number of devices contribute to the training of a common model. We present a method for selecting the devices that provide updates in order to achieve improved generalization, fast convergence, and better device-level performance.
• Score: 31.77382335761709
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a Federated Learning (FL) setup, a number of devices contribute to the training of a common model. We present a method for selecting the devices that provide updates in order to achieve improved generalization, fast convergence, and better device-level performance. We formulate a min-max optimization problem and decompose it into a primal-dual setup, where the duality gap is used to quantify the device-level performance. Our strategy combines \emph{exploration} of data freshness through a random device selection with \emph{exploitation} through simplified estimates of device contributions. This improves the performance of the trained model both in terms of generalization and personalization. A modified Truncated Monte-Carlo (TMC) method is applied during the exploitation phase to estimate the device's contribution and lower the communication overhead. The experimental results show that the proposed approach has a competitive performance, with lower communication overhead and competitive personalization performance against the baseline schemes.

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