Hercules: Boosting the performance of privacy-preserving federated learning

• Main Authors: XU, Guowen, HAN, Xingshuo, XU, Shengmin, ZHANG, Tianwei, LI, Hongwei, HUANG, Xinyi, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: federated learning; polynomial approximation; Privacy protection; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/8397; https://ink.library.smu.edu.sg/context/sis_research/article/9400/viewcontent/2207.04620__1_.pdf
• Institution: Singapore Management University

In this paper, we address the problem of privacy-preserving federated neural network training with N users. We present Hercules, an efficient and high-precision training framework that can tolerate collusion of up to N−1 users. Hercules follows the POSEIDON framework proposed by Sav et al. (NDSS’21)...