4 papers
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
Shiyuan Zuo, Xingrun Yan, Rongfei Fan +4
Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but is vulnerable to Byzantine attacks and data heterogeneity, which can…
Consistent Estimation of Numerical Distributions under Local Differential Privacy by Wavelet Expansion
Puning Zhao, Zhikun Zhang, Bo Sun +4
Distribution estimation under local differential privacy (LDP) is a fundamental and challenging task. Significant progresses have been made on categorical data. However, due to dif…
On Theoretical Limits of Learning with Label Differential Privacy
Puning Zhao, Chuan Ma, Li Shen +2
Label differential privacy (DP) is designed for learning problems involving private labels and public features. While various methods have been proposed for learning under label DP…
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
Puning Zhao, Lifeng Lai, Li Shen +3
Privacy protection of users' entire contribution of samples is important in distributed systems. The most effective approach is the two-stage scheme, which finds a small interval f…