AI 中文总结
本文针对无线网络中联邦遗忘的延迟问题，建立综合系统模型，提出高效迭代算法优化资源分配，可显著降低遗忘完成时间且复杂度为多项式级。
AI 中文摘要
为满足严格的数据隐私法规要求，联邦遗忘（Federated Unlearning, FU）已成为一种关键范式。然而，由于迭代校准需求和物理层信道不确定性，其在无线网络上的实现会带来严重的通信延迟和可靠性挑战。本文研究联邦遗忘网络（Federated Unlearning Networks, FUN）的延迟最小化问题。具体而言，我们建立了一个综合系统模型，该模型联合考虑了FUN算法的收敛行为、本地设备计算动态，以及在有界信道状态信息（Channel State Information, CSI）误差下运行的最坏情况鲁棒传输模型。为解决由此产生的非凸联合资源分配问题，我们提出了一种高效的迭代算法。通过利用系统约束的单调性和凸性，该问题通过对本地精度参数的均匀扫描进行分解，在扫描范围内，利用嵌套二分法和黄金分割搜索确定最优延迟、带宽、功率和计算频率。理论分析和大量数值结果均表明，所提算法具有多项式复杂度，且与传统基准方案相比，能显著降低整体遗忘完成时间。
英文摘要
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.
Comments13 pages, 8 figures
