Over-the-Air Federated Averaging with Limited Power and Privacy Budgets
Na Yan, Kezhi Wang, Cunhua Pan, Kok Keong Chai, Feng Shu, and, Jiangzhou Wang

TL;DR
This paper proposes a novel over-the-air federated averaging scheme that balances communication efficiency, privacy, and learning performance under power and privacy constraints, with analytical and simulation validation.
Contribution
It introduces a joint design framework for device scheduling, alignment, and aggregation rounds in DP-OTA-FedAvg, optimizing for power and privacy budgets.
Findings
Optimal device scheduling and alignment coefficients improve learning accuracy.
The proposed method effectively balances privacy, power, and convergence speed.
Simulation results confirm the theoretical advantages of the approach.
Abstract
To jointly overcome the communication bottleneck and privacy leakage of wireless federated learning (FL), this paper studies a differentially private over-the-air federated averaging (DP-OTA-FedAvg) system with a limited sum power budget. With DP-OTA-FedAvg, the gradients are aligned by an alignment coefficient and aggregated over the air, and channel noise is employed to protect privacy. We aim to improve the learning performance by jointly designing the device scheduling, alignment coefficient, and the number of aggregation rounds of federated averaging (FedAvg) subject to sum power and privacy constraints. We first present the privacy analysis based on differential privacy (DP) to quantify the impact of the alignment coefficient on privacy preservation in each communication round. Furthermore, to study how the device scheduling, alignment coefficient, and the number of the global…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cooperative Communication and Network Coding · Wireless Networks and Protocols
