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
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…