mlsanityData to 14 Oct 2026

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

arXiv 2206.06679 ↗First posted 14 Jun 2022cs.IT cs.LG math.ITcited by 1

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Abstract (arXiv)

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(N2+Kright)3 + N6 while the complexity of the proposed scheme scales with Kp Nq for some 0 < p,q ≤ 2. The efficiency of the proposed scheme is confirmed via numerical experiments on the CIFAR-10 dataset.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 40 of its 89 references that are in the map. First posted on arXiv 14 Jun 2022.

Builds on

40 of 89 references.

References · 40

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28 more
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Citing papers per year: 2022: 1.

1 paper.

  1. 2022A Survey on Over-the-Air Computationcited by 21