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arXiv · 2409.00978

Uplink Over-the-Air Aggregation for Multi-Model Wireless Federated Learning

Chong Zhang·Min Dong·Ben Liang·Ali Afana·Yahia Ahmed
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Abstract

We propose an uplink over-the-air aggregation (OAA) method for wireless federated learning (FL) that simultaneously trains multiple models. To maximize the multi-model training convergence rate, we derive an upper bound on the optimality gap of the global model update, and then, formulate an uplink joint transmit-receive beamforming optimization problem to minimize this upper bound. We solve this problem using the block coordinate descent approach, which admits low-complexity closed-form updates. Simulation results show that our proposed multi-model FL with fast OAA substantially outperforms sequentially training multiple models under the conventional single-model approach.

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Chong Zhang, Min Dong, Ben Liang, Ali Afana, Yahia Ahmed. 2024-09-02. Uplink Over-the-Air Aggregation for Multi-Model Wireless Federated Learning. https://arxiv.org/abs/2409.00978

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