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

• المؤلفون الرئيسيون: CHEN, Zekai, YU, Shengxing, CHEN, Farong, WANG, Fuyi, LIU, Ximeng, DENG, Robert H.
• التنسيق: text
• اللغة: English
• منشور في: Institutional Knowledge at Singapore Management University 2024
• الموضوعات: Distributed machine learning; federated learning; heterogeneity data; privacy preserving; Artificial Intelligence and Robotics; Information Security
• الوصول للمادة أونلاين: https://ink.library.smu.edu.sg/sis_research/9637

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...