3 papers
cs.CR2024
Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learning
Chenfei Nie, Qiang Li, Yuxin Yang +2
Federated learning (FL) is an emerging distributed learning paradigm without sharing participating clients' private data. However, existing works show that FL is vulnerable to both…
cs.LG2023
Certifying the Fairness of KNN in the Presence of Dataset Bias
Yannan Li, Jingbo Wang, Chao Wang
We propose a method for certifying the fairness of the classification result of a widely used supervised learning algorithm, the k-nearest neighbors (KNN), under the assumption tha…
cs.SE2023
Systematic Testing of the Data-Poisoning Robustness of KNN
Yannan Li, Jingbo Wang, Chao Wang
Data poisoning aims to compromise a machine learning based software component by contaminating its training set to change its prediction results for test inputs. Existing methods f…