Federated Unlearning Over Wireless Networks
2026-08-10 • Information Theory
Information Theory
AI summaryⓘ
The authors study how to speed up federated unlearning, a process that helps remove data from a shared learning system to meet privacy rules, when using wireless networks. They built a detailed model that considers how devices learn and communicate over uncertain wireless channels. To solve the tricky optimization problem of minimizing delay, they created an efficient step-by-step algorithm that smartly adjusts factors like communication power and device computing speed. Their tests show this method works faster than traditional approaches while handling real-world network uncertainties.
federated unlearningwireless networksdata privacyresource allocationchannel state informationconvergence behaviornon-convex optimizationalgorithm complexitycommunication latency
Authors
Yixuan Chen, Zhouxiang Zhao, Mingzhe Chen, Wei Xu, Zhaoyang Zhang, Zhaohui Yang
Abstract
To comply with stringent data privacy regulations, federated unlearning (FU) has emerged as a critical paradigm. However, its implementation over wireless networks introduces severe communication latency and reliability challenges due to iterative calibration requirements and physical-layer channel uncertainties. In this paper, we investigate the problem of delay minimization for federated unlearning networks (FUN). Specifically, we establish a comprehensive system model that jointly incorporates the convergence behavior of the FUN algorithm, local device computation dynamics, and a worst-case robust transmission model operating under bounded channel state information (CSI) error. To solve the resulting non-convex joint resource allocation problem, we propose an efficient iterative algorithm. By exploiting the monotonicity and convexity properties of the system constraints, the problem is decomposed via a uniform scan over the local accuracy parameter, within which the optimal delay, bandwidth, power, and computation frequency are determined utilizing nested bisection and golden-section searches. Both theoretical analysis and extensive numerical results demonstrate that the proposed algorithm achieves polynomial complexity and significantly reduces the overall unlearning completion time compared to conventional baseline schemes.