Privacy Enhancement in Over-the-Air Federated Learning via Adaptive Receive Scaling
Faeze Moradi Kalarde, Ben Liang, Min Dong, Yahia A. Eldemerdash Ahmed, Ho Ting Cheng
TL;DR
The paper tackles privacy-aware adaptive receive scaling in over-the-air federated learning under time-varying channels. It introduces AdaScale, an online algorithm that minimizes time-averaged Rényi differential privacy leakage while enforcing a convergence constraint, using a single-round, convex per-round reformulation and a virtual-queue driven Lyapunov-type control. Theoretical results establish bounds on drift, queue length, constraint violation, and dynamic regret, with diminishing regret when $1<eta<2$, implying convergence to a stationary point. Empirical results on MNIST and CIFAR-10 show AdaScale achieving substantial reductions in both RDP and DP leakages without sacrificing learning performance, approaching offline-optimal performance in practice.
Abstract
In Federated Learning (FL) with over-the-air aggregation, the quality of the signal received at the server critically depends on the receive scaling factors. While a larger scaling factor can reduce the effective noise power and improve training performance, it also compromises the privacy of devices by reducing uncertainty. In this work, we aim to adaptively design the receive scaling factors across training rounds to balance the trade-off between training convergence and privacy in an FL system under dynamic channel conditions. We formulate a stochastic optimization problem that minimizes the overall Rényi differential privacy (RDP) leakage over the entire training process, subject to a long-term constraint that ensures convergence of the global loss function. Our problem depends on unknown future information, and we observe that standard Lyapunov optimization is not applicable. Thus, we develop a new online algorithm, termed AdaScale, based on a sequence of novel per-round problems that can be solved efficiently. We further derive upper bounds on the dynamic regret and constraint violation of AdaSacle, establishing that it achieves diminishing dynamic regret in terms of time-averaged RDP leakage while ensuring convergence of FL training to a stationary point. Numerical experiments on canonical classification tasks show that our approach effectively reduces RDP and DP leakages compared with state-of-the-art benchmarks without compromising learning performance.