Privacy-preserving asynchronous federated learning under non-IID settings

• Main Authors: MIAO, Yinbin, KUANG, Da, LI, Xinghua, XU, Shujiang, LI, Hongwei, CHOO, Kim-Kwang Raymond, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Adaptation models; asynchronous; Computational modeling; Data models; Federated learning; Non-IID; Optimization; Privacy; Privacy-preserving; Vectors; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/8819
• Institution: Singapore Management University

To address the challenges posed by data silos and heterogeneity in distributed machine learning, privacy-preserving asynchronous Federated Learning (FL) has been extensively explored in academic and industrial fields. However, existing privacy-preserving asynchronous FL schemes still suffer from the...