3 citations · 9 across the 9 of their papers we have counts for
5 papers · 1 filter
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Shiqi He, Qifan Yan, Feijie Wu +3
Federated learning (FL) is an effective technique to directly involve edge devices in machine learning training while preserving client privacy. However, the substantial communicat…
Intrinsic Bias Identification on Medical Image Datasets
Shijie Zhang, Lanjun Wang, Lian Ding +3
Machine learning based medical image analysis highly depends on datasets. Biases in the dataset can be learned by the model and degrade the generalizability of the applications. Th…
Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation
Saeed Ranjbar Alvar, Lanjun Wang, Jian Pei +1
Image-to-image translation models are shown to be vulnerable to the Membership Inference Attack (MIA), in which the adversary's goal is to identify whether a sample is used to trai…
Targeted Data Poisoning Attack on News Recommendation System by Content Perturbation
Xudong Zhang, Zan Wang, Jingke Zhao +1
News Recommendation System(NRS) has become a fundamental technology to many online news services. Meanwhile, several studies show that recommendation systems(RS) are vulnerable to…
Cosine Model Watermarking Against Ensemble Distillation
Laurent Charette, Lingyang Chu, Yizhou Chen +3
Many model watermarking methods have been developed to prevent valuable deployed commercial models from being stealthily stolen by model distillations. However, watermarks produced…