1 citations · 3 across the 20 of their papers we have counts for
12 papers · 1 filter
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
Puning Zhao, Jiafei Wu, Zhe Liu +3
We study convex optimization problems under differential privacy (DP). With heavy-tailed gradients, existing works achieve suboptimal rates. The main obstacle is that existing grad…
Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets
Xingrun Yan, Shiyuan Zuo, Rongfei Fan +4
In a real federated learning (FL) system, communication overhead for passing model parameters between the clients and the parameter server (PS) is often a bottleneck. Hierarchical…
Contextual Bandits for Unbounded Context Distributions
Puning Zhao, Rongfei Fan, Shaowei Wang +4
Nonparametric contextual bandit is an important model of sequential decision making problems. Under -Tsybakov margin condition, existing research has established a regret bound…
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…
Learning with User-Level Local Differential Privacy
Puning Zhao, Li Shen, Rongfei Fan +4
User-level privacy is important in distributed systems. Previous research primarily focuses on the central model, while the local models have received much less attention. Under th…
Enhancing Learning with Label Differential Privacy by Vector Approximation
Puning Zhao, Rongfei Fan, Huiwen Wu +3
Label differential privacy (DP) is a framework that protects the privacy of labels in training datasets, while the feature vectors are public. Existing approaches protect the priva…
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
Huiwen Wu, Xiaogang Xu, Deyi Zhang +3
The success of current Large-Language Models (LLMs) hinges on extensive training data that is collected and stored centrally, called Centralized Learning (CL). However, such a coll…
Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning
Qingming Li, Juzheng Miao, Puning Zhao +5
In federated learning, client selection is a critical problem that significantly impacts both model performance and fairness. Prior studies typically treat these two objectives sep…
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…
Soft Label PU Learning
Yong Lei, Puning Zhao, Jintao Deng +1
PU learning refers to the classification problem in which only part of positive samples are labeled. Existing PU learning methods treat unlabeled samples equally. However, in many…
Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
Shiyuan Zuo, Xingrun Yan, Rongfei Fan +4
This paper addresses federated learning (FL) in the context of malicious Byzantine attacks and data heterogeneity. We introduce a novel Robust Average Gradient Algorithm (RAGA), wh…
Learn Suspected Anomalies from Event Prompts for Video Anomaly Detection
Chenchen Tao, Xiaohao Peng, Chong Wang +4
Most models for weakly supervised video anomaly detection (WS-VAD) rely on multiple instance learning, aiming to distinguish normal and abnormal snippets without specifying the typ…