联邦生成对抗网络中的隐私保护技术研究综述OA

Federated generative adversarial networks(FedGAN)have emerged as a promising privacy-preserving frame-work by integrating generative modeling with federated learning.Unlike traditional centralized GAN,FedGAN enables joint model training without exposing raw data,thereby reducing the risk of privacy leakage.However,due to the intrinsic capability of generative models to reconstruct data distributions,FedGAN remains vulnerable to various privacy attacks such as membership inference and model extraction.This paper provides a comprehensive overview of privacy attack models in FedGAN and surveys the research progress of differential privacy mechanisms within this framework.Specifi-cally,it analyzes the architectural characteristics of FedGAN and the implementation of mainstream privacy-preserving techniques,including differentially private stochastic gradient descent(DPSGD)and Rényi differential privacy(RDP).The paper also briefly introduces the recent applications of knowledge distillation,homomorphic encryption,and secures multi-party computation in the FedGAN context.Finally,this paper summarizes the current challenges and discusses potential future research directions.