mlsanityData to 7 Oct 2026

Joint Device Selection and Power Control for Wireless Federated Learning

arXiv 2205.09306 ↗First posted 19 May 2022cs.IT eess.SP math.ITcited by 19

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Abstract (arXiv)

This paper studies the joint device selection and power control scheme for wireless federated learning (FL), considering both the downlink and uplink communications between the parameter server (PS) and the terminal devices. In each round of model training, the PS first broadcasts the global model to the terminal devices in an analog fashion, and then the terminal devices perform local training and upload the updated model parameters to the PS via over-the-air computation (AirComp). First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices and which enables the joint learning and communication optimization simply by the device selection and power control. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and the upper bound on the expected optimality gap between the expected and optimal global loss values is derived. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problems under both the individual and sum uplink transmit power constraints, respectively, which are shown to be solved by the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the ideal FedAvg scheme with error-free model exchange and full device participation.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 May 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 5; 2025: 5; 2026: 1.

19 papers.

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  1. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  2. 2022A Survey on Over-the-Air Computationcited by 19
  3. 2024An I2I Inpainting Approach for Efficient Channel Knowledge Map Constructioncited by 13
  4. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  5. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  6. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
  7. 2024Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocationcited by 3
  8. 2025Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learningcited by 3
  9. 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systemscited by 1
  10. 2023Over-the-Air Federated Averaging with Limited Power and Privacy Budgetscited by 1
  11. 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecturecited by 1
  12. 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Upcited by 1
7 more
  1. 2022Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descentcited by 0
  2. 2023Semi-Asynchronous Federated Edge Learning Mechanism via Over-the-air Computationcited by 0
  3. 2023REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devicescited by 0
  4. 2024FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selectioncited by 0
  5. 2025Federated learning over physical channels: adaptive algorithms with near-optimal guaranteescited by 0
  6. 2025Timely Parameter Updating in Over-the-Air Federated Learningcited by 0
  7. 2026Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networkscited by 0