mlsanityData to 7 Oct 2026

Power Allocation for Wireless Federated Learning using Graph Neural Networks

arXiv 2111.07480 ↗First posted 15 Nov 2021cs.LG cs.NI eess.SPcited by 7

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Abstract (arXiv)

We propose a data-driven approach for power allocation in the context of federated learning (FL) over interference-limited wireless networks. The power policy is designed to maximize the transmitted information during the FL process under communication constraints, with the ultimate objective of improving the accuracy and efficiency of the global FL model being trained. The proposed power allocation policy is parameterized using a graph convolutional network and the associated constrained optimization problem is solved through a primal-dual algorithm. Numerical experiments show that the proposed method outperforms three baseline methods in both transmission success rate and FL global performance.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 15 Nov 2021.

Builds on

17 of 29 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  3. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  4. 2019Stability Properties of Graph Neural Networkscited by 125
  5. 2019Optimal Wireless Resource Allocation with Random Edge Graph Neural Networkscited by 90
  6. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  7. 2020Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocationcited by 61
  8. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 56
  9. 2018Learning Optimal Resource Allocations in Wireless Systemscited by 39
  10. 2020Distributed Scheduling using Graph Neural Networkscited by 27
  11. 2019A Graph Neural Network Approach for Scalable Wireless Power Controlcited by 26
  12. 2021Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networkscited by 14
5 more
  1. 2020Adaptive Contention Window Design using Deep Q-learningcited by 13
  2. 2020MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networkscited by 11
  3. 2020Quantizing data for distributed learningcited by 7
  4. 2021ML-aided power allocation for Tactical MIMOcited by 6
  5. 2020Communication-Efficient Robust Federated Learning Over Heterogeneous Datasetscited by 4

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Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 1; 2025: 1.

7 papers.

  1. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  2. 2022State-Augmented Learnable Algorithms for Resource Management in Wireless Networkscited by 13
  3. 2023Stochastic Unrolled Federated Learningcited by 6
  4. 2023Learning to Transmit with Provable Guarantees in Wireless Federated Learningcited by 3
  5. 2025Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regressioncited by 3
  6. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  7. 2022A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networkscited by 0