Efficient Privacy-Preserving Federated Learning With Improved Compressed Sensing

• Main Authors: ZHANG, Yifan., MIAO, Yinbin., LI, Xinghua., WEI, Linfeng., LIU, Zhiquan, CHOO, Kim-Kwang R., DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: CKKS; communication costs; compression sensing (CS); federated learning (FL); homomorphic encryption; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/8291
• Institution: Singapore Management University

To solve the data silos issue in distributed machine learning with privacy leakage, privacy-preserving federated learning (PPFL) has been extensively explored in both academic and industrial fields. However, the existing PPFL solutions still suffer from high computation and communication overheads,...