# Joint Device Selection and Power Control for Wireless Federated Learning

arXiv: https://arxiv.org/abs/2205.09306 · HTML: https://mlsanity.com/paper/2205.09306/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2022.

## Built on by (most cited first, 20 of 20)

- [A Survey on Over-the-Air Computation](https://mlsanity.com/paper/2210.11350/) (2022)
- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction](https://mlsanity.com/paper/2406.09822/) (2024)
- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [Over-the-Air Federated Learning via Weighted Aggregation](https://mlsanity.com/paper/2409.07822/) (2024)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation](https://mlsanity.com/paper/2412.07428/) (2024)
- [Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning](https://mlsanity.com/paper/2501.01828/) (2025)
- [Digital Over-the-Air Federated Learning in Multi-Antenna Systems](https://mlsanity.com/paper/2302.14648/) (2023)
- [Over-the-Air Federated Edge Learning with Error-Feedback One-Bit Quantization and Power Control](https://mlsanity.com/paper/2303.11319/) (2023)
- [Over-the-Air Federated Averaging with Limited Power and Privacy Budgets](https://mlsanity.com/paper/2305.03547/) (2023)
- [Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture](https://mlsanity.com/paper/2503.18284/) (2025)
- [Rethinking Federated Learning Over the Air: The Blessing of Scaling Up](https://mlsanity.com/paper/2508.17697/) (2025)
- [Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent](https://mlsanity.com/paper/2210.03444/) (2022)
- [Semi-Asynchronous Federated Edge Learning Mechanism via Over-the-air Computation](https://mlsanity.com/paper/2305.04066/) (2023)
- [REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices](https://mlsanity.com/paper/2309.13643/) (2023)
- [FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection](https://mlsanity.com/paper/2407.00943/) (2024)
- [Federated learning over physical channels: adaptive algorithms with near-optimal guarantees](https://mlsanity.com/paper/2509.02538/) (2025)
- [Timely Parameter Updating in Over-the-Air Federated Learning](https://mlsanity.com/paper/2512.19103/) (2025)
- [Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks](https://mlsanity.com/paper/2605.30892/) (2026)

## 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.