This work studies training generative adversarial networks under the federated learning setting. Generative adversarial networks (GANs) have achieved advancement in various real-world applications, such as image editing, style transfer, scene generations, etc. However, like other deep learning models, ...
Creswell A et al (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53–65 Article Google Scholar Fan C, Liu P (2020) Federated generative adversarial learning. In: Chinese conference on pattern recognition and computer vision (PRCV). Springer Rasouli M, Sun...
(论文链接:Protection Against Reconstruction and Its Applications in Private Federated Learning) 5.1.3 基于GAN的推理攻击 GAN是生成对抗网络(Generative Adversarial Networks),近年来在大数据领域广受欢迎,也适用于基于联邦学习的方法。特别针对联邦学习,有研究者提出了 mGAN-AI 框架来探索基于 GAN 的联邦学习攻击。
Abstract: Minimax problems arise in a wide range of important applications including robust adversarial learning and Generative Adversarial Network (GAN) training. Recently, algorithms for minimax problems in the Federated Learning (FL) paradigm have received considerable interest. Existing federated algorithm...
In addition, the results show Byzantine-resilient aggregation technology is weak to offense this type of attack in the federated setting. Then Zhang, Chen, Wu, Chen, and Yu (2019) give first attempt to generate model poisoning attack based on Generative Adversarial Nets (GAN). In this work,...
dataset are not pre-set during model learning, but rather determined through the data features such as principal component analysis25, random forest26, and more. This classification is widely used promisingly and is currently composed of differential privacy, along with generative adversarial networks27...
MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets. 2018-11-09 (GAN) Federated Generative Adversarial Learning. 2020-05-07 Citation: 0 (VAE) An On-Device Federated Learning Approach for Cooperative Anomaly Detection Federated Extra-Trees with Privacy Preserving (Clusterin...
There are certain common types of FL: federated reinforcement learning (FRL), federated supervised learning (FSL), FL for generative adversarial networks (GANs) (unsupervised learning), and FL for contrastive learning (self-supervised learning). In Refs. [23], [24], the goal of FRL is to en...
Zhang J, Chen J, Wu D et al (2019) Poisoning attack in federated learning using generative adversarial nets. In: Proceedings of the 18th IEEE international conference on trust, security and privacy in computing and communications/13th IEEE international conference on big data science and engineering...
联邦学习在ICLR 2023会议中的论文清单 [1]如下: A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy作者 Kaan Ozkara 作者 Antonious M. Girgis 作…