FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning

FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning

9 February 2024

Hanlin Gu

Papers citing "FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning"

4 / 4 papers shown

Title
One-Shot Clustering for Federated Learning Maciej Krzysztof Zuziak Roberto Pellungrini Salvatore Rinzivillo FedML 81 0 0 06 Mar 2025
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning Yan Kang Hanlin Gu Xingxing Tang Yuanqin He Yuzhu Zhang Jinnan He Yuxing Han Lixin Fan Kai Chen Qiang Yang FedML 63 18 0 29 Apr 2023
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 185 357 0 24 Mar 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 157 756 0 28 Sep 2019