Multi-task Over-the-Air Federated Learning: A Non-Orthogonal Transmission Approach

• URL: http://arxiv.org/abs/2106.14229v2
• Date: Tue, 29 Jun 2021 07:34:10 GMT
• Title: Multi-task Over-the-Air Federated Learning: A Non-Orthogonal Transmission Approach
• Authors: Haoming Ma, Xiaojun Yuan, Dian Fan, Zhi Ding, Xin Wang
• Abstract summary: We propose a multi-task over-theair federated learning (MOAFL) framework, where multiple learning tasks share edge devices for data collection and learning models under the coordination of a edge server (ES) Both the convergence analysis and numerical results demonstrate that the MOAFL framework can significantly reduce the uplink bandwidth consumption of multiple tasks without causing substantial learning performance degradation.
• Score: 52.85647632037537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this letter, we propose a multi-task over-theair federated learning (MOAFL) framework, where multiple learning tasks share edge devices for data collection and learning models under the coordination of a edge server (ES). Specially, the model updates for all the tasks are transmitted and superpositioned concurrently over a non-orthogonal uplink channel via over-the-air computation, and the aggregation results of all the tasks are reconstructed at the ES through an extended version of the turbo compressed sensing algorithm. Both the convergence analysis and numerical results demonstrate that the MOAFL framework can significantly reduce the uplink bandwidth consumption of multiple tasks without causing substantial learning performance degradation.

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