mlsanityData to 14 Oct 2026

Learning to Transmit with Provable Guarantees in Wireless Federated Learning

arXiv 2304.09329 ↗First posted 18 Apr 2023cs.LG cs.IT cs.SYcited by 3

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Abstract (arXiv)

We propose a novel data-driven approach to allocate transmit power for federated learning (FL) over interference-limited wireless networks. The proposed method is useful in challenging scenarios where the wireless channel is changing during the FL training process and when the training data are not independent and identically distributed (non-i.i.d.) on the local devices. Intuitively, the power policy is designed to optimize the information received at the server end during the FL process under communication constraints. Ultimately, our goal is to improve the accuracy and efficiency of the global FL model being trained. The proposed power allocation policy is parameterized using graph convolutional networks (GCNs), and the associated constrained optimization problem is solved through a primal-dual (PD) algorithm. Theoretically, we show that the formulated problem has a zero duality gap and, once the power policy is parameterized, optimality depends on how expressive this parameterization is. Numerically, we demonstrate that the proposed method outperforms existing baselines under different wireless channel settings and varying degrees of data heterogeneity.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 31 of its 75 references that are in the map. First posted on arXiv 18 Apr 2023.

Builds on

31 of 75 references.

References · 31

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19 more
  1. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  2. 2020Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocationcited by 61
  3. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 57
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  9. 2021Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networkscited by 14
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 2024Biased Backpressure Routing Using Link Features and Graph Neural Networkscited by 2
  2. 2024Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrivalcited by 1
  3. 2026Polar Code Based Federated Learning: Convergence Analysis and Resource Allocationcited by 0