1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Tackling Privacy Heterogeneity in Differentially Private Federated Learning
Ruichen Xu, Ying-Jun Angela Zhang, Jianwei Huang
Differentially private federated learning (DP-FL) enables clients to collaboratively train machine learning models while preserving the privacy of their local data. However, most e…
cs.LG2026
JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning
Ruichen Xu, Ying-Jun Angela Zhang, Jianwei Huang
Differentially private federated learning faces a fundamental tension: privacy protection mechanisms that safeguard client data simultaneously create quantifiable privacy costs tha…
cs.LG2021★ 1 cited
Federated Few-Shot Learning with Adversarial Learning
Chenyou Fan, Jianwei Huang
We are interested in developing a unified machine learning model over many mobile devices for practical learning tasks, where each device only has very few training data. This is a…