61 citations · 98 across the 13 of their papers we have counts for
6 papers · 1 filter
EIFFeL: Ensuring Integrity for Federated Learning
Amrita Roy Chowdhury, Chuan Guo, Somesh Jha +1
Federated learning (FL) enables clients to collaborate with a server to train a machine learning model. To ensure privacy, the server performs secure aggregation of updates from th…
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Weizhe Hua, Yichi Zhang, Chuan Guo +2
Neural network robustness has become a central topic in machine learning in recent years. Most training algorithms that improve the model's robustness to adversarial and common cor…
Online Adaptation to Label Distribution Shift
Ruihan Wu, Chuan Guo, Yi Su +1
Machine learning models often encounter distribution shifts when deployed in the real world. In this paper, we focus on adaptation to label distribution shift in the online setting…
Byzantine-Robust and Privacy-Preserving Framework for FedML
Hanieh Hashemi, Yongqin Wang, Chuan Guo +1
Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients. This learning setting presents, among others…
Making Paper Reviewing Robust to Bid Manipulation Attacks
Ruihan Wu, Chuan Guo, Felix Wu +3
Most computer science conferences rely on paper bidding to assign reviewers to papers. Although paper bidding enables high-quality assignments in days of unprecedented submission n…
Measuring Data Leakage in Machine-Learning Models with Fisher Information
Awni Hannun, Chuan Guo, Laurens van der Maaten
Machine-learning models contain information about the data they were trained on. This information leaks either through the model itself or through predictions made by the model. Co…