Training Differentially Private Models with Secure Multiparty Computation

5 February 2022

Papers citing "Training Differentially Private Models with Secure Multiparty Computation"

4 / 4 papers shown

Title
ByzSFL: Achieving Byzantine-Robust Secure Federated Learning with Zero-Knowledge Proofs Yongming Fan Rui Zhu Zihao Wang Chenghong Wang Haixu Tang Ye Dong Hyunghoon Cho Lucila Ohno-Machado 45 0 0 12 Jan 2025
DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum Xiaolan Gu Ming Li Lishuang Xiong FedML 32 4 0 22 Jun 2023
Confidential Truth Finding with Multi-Party Computation (Extended Version) Angelo Saadeh Pierre Senellart S. Bressan HILM FedML 21 1 0 24 May 2023
Semi-Private Computation of Data Similarity with Applications to Data Valuation and Pricing René Bødker Christensen Shashi Raj Pandey P. Popovski FedML 24 4 0 14 Jun 2022