Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications

12 October 2020

Papers citing "Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications"

10 / 10 papers shown

Title
Identify Backdoored Model in Federated Learning via Individual Unlearning Jiahao Xu Zikai Zhang Rui Hu FedML AAML 77 1 0 01 Nov 2024
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective Xianghua Xie Chen Hu Hanchi Ren Jingjing Deng FedML AAML 58 19 0 27 Nov 2023
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges Vishnu Pandi Chellapandi Liangqi Yuan Christopher G. Brinton Stanislaw H. .Zak Ziran Wang FedML 43 77 0 21 Aug 2023
GAN-based Vertical Federated Learning for Label Protection in Binary Classification Yujin Han Leying Guan FedML 40 0 0 04 Feb 2023
Training Differentially Private Models with Secure Multiparty Computation Sikha Pentyala Davis Railsback Ricardo Maia Rafael Dowsley David Melanson Anderson C. A. Nascimento Martine De Cock 21 14 0 05 Feb 2022
Secret Sharing Sharing For Highly Scalable Secure Aggregation Timothy Stevens Joseph P. Near Christian Skalka FedML 24 5 0 03 Jan 2022
JUBILEE: Secure Debt Relief and Forgiveness David Sánchez 21 0 0 15 Sep 2021
FedLab: A Flexible Federated Learning Framework Dun Zeng Siqi Liang Xiangjing Hu Hui Wang Zenglin Xu FedML 13 107 0 24 Jul 2021
AI in Finance: Challenges, Techniques and Opportunities LongBing Cao AIFin 41 241 0 20 Jul 2021
Federated Noisy Client Learning Huazhu Fu Li Li Bo Han Chengzhong Xu Ling Shao FedML 28 26 0 24 Jun 2021