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.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Federated learning; fully homomorphic encryption; GAN; non-IID; user-level privacy; Databases and Information Systems; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/8743; https://ink.library.smu.edu.sg/context/sis_research/article/9746/viewcontent/FlGan_av.pdf
• Institution: 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...