Privacy-preserving federated deep learning with irregular users

• Main Authors: XU, Guowen, LI, Hongwei, ZHANG, Yun, XU, Shengmin, NING, Jianting, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Collaborative learning; deep learning; privacy; Information Security; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/5181; https://ink.library.smu.edu.sg/context/sis_research/article/6184/viewcontent/Privacy_Preserving_Federated_Deep_Learning_Irregular_Users_2020_av.pdf
• Institution: Singapore Management University

Federated deep learning has been widely used in various fields. To protect data privacy, many privacy-preserving approaches have also been designed and implemented in various scenarios. However, existing works rarely consider a fundamental issue that the data shared by certain users (called irregula...