Comments on "Federated Learning with Differential Privacy: Algorithms and Performance Analysis"
分布式、并行与集群计算
2024-06-11 v1 密码学与安全
性能
摘要
In the paper by Wei et al. ("Federated Learning with Differential Privacy: Algorithms and Performance Analysis"), the convergence performance of the proposed differential privacy algorithm in federated learning (FL), known as Noising before Model Aggregation FL (NbAFL), was studied. However, the presented convergence upper bound of NbAFL (Theorem 2) is incorrect. This comment aims to present the correct form of the convergence upper bound for NbAFL.
引用
@article{arxiv.2406.05858,
title = {Comments on "Federated Learning with Differential Privacy: Algorithms and Performance Analysis"},
author = {Mahtab Talaei and Iman Izadi},
journal= {arXiv preprint arXiv:2406.05858},
year = {2024}
}