8 citations · 23 across the 13 of their papers we have counts for
12 papers · 1 filter
Probably Approximately Correct Federated Learning
Xiaojin Zhang, Anbu Huang, Lixin Fan +2
Federated learning (FL) is a new distributed learning paradigm, with privacy, utility, and efficiency as its primary pillars. Existing research indicates that it is unlikely to sim…
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning
Xiaojin Zhang, Yan Kang, Lixin Fan +2
Trustworthy Federated Learning (TFL) typically leverages protection mechanisms to guarantee privacy. However, protection mechanisms inevitably introduce utility loss or efficiency…
Theoretically Principled Federated Learning for Balancing Privacy and Utility
Xiaojin Zhang, Wenjie Li, Yiming Li +3
We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and uti…
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
Xiaojin Zhang, Kai Chen, Qiang Yang
Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protecti…
A Game-theoretic Framework for Privacy-preserving Federated Learning
Xiaojin Zhang, Lixin Fan, Siwei Wang +3
In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of \textit{privacy leakage} cannot be ignored in the presence of \texti…
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
Yan Kang, Jiahuan Luo, Yuanqin He +3
Federated learning (FL) has emerged as a practical solution to tackle data silo issues without compromising user privacy. One of its variants, vertical federated learning (VFL), ha…
Trading Off Privacy, Utility and Efficiency in Federated Learning
Xiaojin Zhang, Yan Kang, Kai Chen +2
Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protecti…
No Free Lunch Theorem for Security and Utility in Federated Learning
Xiaojin Zhang, Hanlin Gu, Lixin Fan +2
In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms…
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei +2
In this work, we develop linear bandit algorithms that automatically adapt to different environments. By plugging a novel loss estimator into the optimization problem that characte…
Adaptive Double-Exploration Tradeoff for Outlier Detection
Xiaojin Zhang, Honglei Zhuang, Shengyu Zhang +1
We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold…
Automatic Ensemble Learning for Online Influence Maximization
Xiaojin Zhang
We consider the problem of selecting a seed set to maximize the expected number of influenced nodes in the social network, referred to as the \textit{influence maximization} (IM) p…
Contextual Combinatorial Conservative Bandits
Xiaojin Zhang, Shuai Li, Weiwen Liu +1
The problem of multi-armed bandits (MAB) asks to make sequential decisions while balancing between exploitation and exploration, and have been successfully applied to a wide range…