FlGan: GAN-based unbiased federated learning under non-IID settings

• Main Authors: MA, Zhuoran, LIU, Yang, MIAO, Yinbin, XU, Guowen, LIU, Ximeng, MA, Jianfeng, DENG, Robert H.
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2024
• 主題: Federated learning; fully homomorphic encryption; GAN; non-IID; user-level privacy; Databases and Information Systems; Information Security
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/9639
• 機構: Singapore Management University

Federated Learning (FL) suffers from low convergence and significant accuracy loss due to local biases caused by non-Independent and Identically Distributed (non-IID) data. To enhance the non-IID FL performance, a straightforward idea is to leverage the Generative Adversarial Network (GAN) to mitiga...