Preserving Privacy and Security in Federated Learning

7 February 2022

Papers citing "Preserving Privacy and Security in Federated Learning"

8 / 8 papers shown

Title
Privacy-Preserving and Trustworthy Deep Learning for Medical Imaging Kiarash Sedghighadikolaei Attila A Yavuz 39 1 0 29 Jun 2024
Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach Qiqing Wang Kaidi Yang FedML 34 8 0 22 Jan 2024
zkDFL: An efficient and privacy-preserving decentralized federated learning with zero-knowledge proof Mojtaba Ahmadi Reza Nourmohammadi 22 7 0 01 Dec 2023
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning Tre' R. Jeter Truc D. T. Nguyen Raed Alharbi My T. Thai AAML 24 0 0 23 Nov 2023
Federated Generative Learning with Foundation Models Jie Zhang Xiaohua Qi Bo-Lu Zhao FedML 39 21 0 28 Jun 2023
Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications Ege C. Kaya M. Sahin Abolfazl Hashemi 25 4 0 09 Jun 2023
Active Membership Inference Attack under Local Differential Privacy in Federated Learning Truc D. T. Nguyen Phung Lai K. Tran Nhathai Phan My T. Thai FedML 29 18 0 24 Feb 2023
When the Curious Abandon Honesty: Federated Learning Is Not Private Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML AAML 69 181 0 06 Dec 2021