Lightweight privacy-preserving cross-cluster federated learning with heterogeneous data

• Main Authors: CHEN, Zekai, YU, Shengxing, CHEN, Farong, WANG, Fuyi, LIU, Ximeng, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Distributed machine learning; federated learning; heterogeneity data; privacy preserving; Artificial Intelligence and Robotics; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/9637
• Institution: Singapore Management University

Federated Learning (FL) eliminates data silos that hinder digital transformation while training a shared global model collaboratively. However, training a global model in the context of FL has been highly susceptible to heterogeneity and privacy concerns due to discrepancies in data distribution, wh...