Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning

• URL: http://arxiv.org/abs/2312.12417v1
• Date: Fri, 15 Dec 2023 03:04:39 GMT
• Title: Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning
• Authors: Fan Zhang, Jining Chen, Kunlun Wang, and Wen Chen
• Abstract summary: Federated learning (FL) leverages data distributed at the edge of the network to enable intelligent applications. In this paper, we propose a relay-assisted FL framework, and investigate the device scheduling problem in relay-assisted FL systems.
• Score: 9.735236606901038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) leverages data distributed at the edge of the network to enable intelligent applications. The efficiency of FL can be improved by using over-the-air computation (AirComp) technology in the process of gradient aggregation. In this paper, we propose a relay-assisted large-scale FL framework, and investigate the device scheduling problem in relay-assisted FL systems under the constraints of power consumption and mean squared error (MSE). we formulate a joint device scheduling, and power allocation problem to maximize the number of scheduled devices. We solve the resultant non-convex optimization problem by transforming the optimization problem into multiple sparse optimization problems. By the proposed device scheduling algorithm, these sparse sub-problems are solved and the maximum number of federated learning edge devices is obtained. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.

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