28 citations · 49 across the 17 of their papers we have counts for
3 papers · 1 filter
PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Framework
Weiqi Yue, Wenbiao Li, Yuzhou Jiang +3
Federated learning enables collaborative model training without sharing raw data, but data heterogeneity consistently challenges the performance of the global model. Traditional op…
In-Context Bias Propagation in LLM-Based Tabular Data Generation
Pol G. Recasens, Alberto Gutierrez, Jordi Torres +4
Large Language Models (LLMs) are increasingly used for synthetic tabular data generation through in-context learning (ICL), offering a practical solution for data augmentation in d…
Privacy-Preserving Model and Preprocessing Verification for Machine Learning
Wenbiao Li, Anisa Halimi, Xiaoqian Jiang +2
This paper presents a framework for privacy-preserving verification of machine learning models, focusing on models trained on sensitive data. Integrating Local Differential Privacy…