Optimal Power Control for Over-The-Air Federated Edge Learning Using Statistical Channel Knowledge
Abstract
Over-the-air federated edge learning (OTA-FEEL) is an efficient distributed machine learning framework in terms of radio resource requirements. Delicate power control is needed to combat the distortion of the local models caused by channel fading. This paper develops the optimal power control policy under fading channels, to minimize the optimality gap of OTA-FEEL by exploiting the statistical channel state information (CSI), i.e., the mean and variance of the fading. We reveal that the optimal power control policy takes a form where the variance of the effective channel between the server and devices should be minimized when its mean is given. Based on this structure, we propose to iteratively minimize the variance using the Lagrange-duality method and then optimize the mean of the effective channels using a one-dimensional search. A closed-form expression for the optimal power control is derived. Simulations confirm the benefit of the use of the statistical CSI, and the superiority of the proposed optimal power control policy to the existing approaches in the convergence of OTA-FEEL.
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The emerging OTA-WFL may pose new challenges to existing aggregation-phase defenses because the FL server only accesses the aggregated global model, obscuring local models and allowing backdoor attacks to take place unnoticed.
“the emerging OTA-WFL may pose new challenges to the existing defense mechanisms designed for the aggregation phase due to the fact that the FL server would only access the aggregated global model under OTA-WFL [14], [101]. The local models of individual clients are obscured. While this helps improve the privacy of the individuals' local models to a great extent, it allows backdoor attacks to take”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [14]
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Wan, Y., Qu, Y., Ni, W., Xiang, Y., Gao, L., & Hossain, E. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361451 · Cited by 152 · see citing works
“Inaddition to the above-mentioned limitations, the emerging OTA-WFL may pose new challenges to the existing defense mechanisms designed for the aggregation phase due to the fact that the FL server would only access the aggregated global model under OTA-WFL [14], [101].”
BackgroundSelf-citationOver-the-air federated learning: Status quo, open challenges, and future directions
Xiao, B., Yu, X., Ni, W., Wang, X., & Poor, H. V. (2024) · Fundamental Research · doi:10.1016/j.fmre.2024.01.011 · Cited by 46 · see citing works
Self-citationMulti-Carrier NOMA-Empowered Wireless Federated Learning With Optimal Power and Bandwidth Allocation
Li, W.-C., Lv, T.-J., Cao, Y., Ni, W., & Peng, M. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3273312 · Cited by 22 · see citing works
Self-citationA Retransmission Framework for Over-the-Air Computation Under Time-Varying Channel Fading
Shi, S., Lei, G., Wang, F., Li, Y., Ni, W., & Jamalipour, A. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3547368 · Cited by 4 · see citing works
Self-citation
- Authors
- Yu, X., Xiao, B., Ni, W., & Wang, X.
- Affiliations
- Fudan University (CN)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Venue
- 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)
- Publisher
- IEEE
- Published
- 2022-11-01
- OpenAlex
- W4321064137
- Cited by
- 4
- References
- 14
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1
- FWCI
- 0.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Wireless Communications and Signal Processing (WCSP) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Keywords
- power control, statistical channel state information, fading channels, Lagrange duality, distributed learning, radio resource allocation
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