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

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2025

Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective

Yuan Yao, Xiaopu Zhang, Yu Zhang +2

Semi-supervised heterogeneous domain adaptation (SHDA) addresses learning across domains with distinct feature representations and distributions, where source samples are labeled w…

cs.CR2024

FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation

Hanlin Gu, Jiahuan Luo, Yan Kang +5

Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The conce…

cs.LG2024★ 2 cited

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

Gongxi Zhu, Donghao Li, Hanlin Gu +3

Federated Learning (FL) is a promising approach for training machine learning models on decentralized data while preserving privacy. However, privacy risks, particularly Membership…