Matching Pursuit Based Scheduling for Over-the-Air Federated Learning

Ali Bereyhi; Adela Vagollari; Saba Asaad; Ralf R. Müller; Wolfgang Gerstacker; H. Vincent Poor

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arXiv · 2206.06679

Matching Pursuit Based Scheduling for Over-the-Air Federated Learning

Ali Bereyhi·Adela Vagollari·Saba Asaad·Ralf R. Müller·Wolfgang Gerstacker·H. Vincent Poor

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Abstract

This paper develops a class of low-complexity device scheduling algorithms for over-the-air federated learning via the method of matching pursuit. The proposed scheme tracks closely the close-to-optimal performance achieved by difference-of-convex programming, and outperforms significantly the well-known benchmark algorithms based on convex relaxation. Compared to the state-of-the-art, the proposed scheme poses a drastically lower computational load on the system: For $K$ devices and $N$ antennas at the parameter server, the benchmark complexity scales with $\left(N^2+K\right)^3 + N^6$ while the complexity of the proposed scheme scales with $K^p N^q$ for some $0 < p,q \leq 2$. The efficiency of the proposed scheme is confirmed via numerical experiments on the CIFAR-10 dataset.

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Ali Bereyhi, Adela Vagollari, Saba Asaad, Ralf R. Müller, Wolfgang Gerstacker, H. Vincent Poor. 2022-06-14. Matching Pursuit Based Scheduling for Over-the-Air Federated Learning. https://arxiv.org/abs/2206.06679

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