10 citations · 15 across the 5 of their papers we have counts for
5 papers
Temporal Gradient Inversion Attacks with Robust Optimization
Bowen Li, Hanlin Gu, Ruoxin Chen +5
Federated Learning (FL) has emerged as a promising approach for collaborative model training without sharing private data. However, privacy concerns regarding information exchanged…
FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders
Hanlin Gu, Lixin Fan, Xingxing Tang +1
This paper proposes a general spectral analysis framework that thwarts a security risk in federated Learning caused by groups of malicious Byzantine attackers or colluders, who con…
FadMan: Federated Anomaly Detection across Multiple Attributed Networks
Nannan Wu, Ning Zhang, Wenjun Wang +2
Anomaly subgraph detection has been widely used in various applications, ranging from cyber attack in computer networks to malicious activities in social networks. Despite an incre…
Federated Deep Learning with Bayesian Privacy
Hanlin Gu, Lixin Fan, Bowen Li +3
Federated learning (FL) aims to protect data privacy by cooperatively learning a model without sharing private data among users. For Federated Learning of Deep Neural Network with…
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack
Ding Sheng Ong, Chee Seng Chan, Kam Woh Ng +2
Ever since Machine Learning as a Service (MLaaS) emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) ha…