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cs.LG2026
Differential Privacy in Two-Layer Networks: How DP-SGD Harms Fairness and Robustness
Ruichen Xu, Kexin Chen
Differentially private learning is essential for training models on sensitive data, but empirical studies consistently show that it can degrade performance, introduce fairness issu…
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…