Search Results for author: Xiaojin Zhang

Found 27 papers, 5 papers with code

RSL-SQL: Robust Schema Linking in Text-to-SQL Generation

1 code implementation31 Oct 2024 Zhenbiao Cao, Yuanlei Zheng, Zhihao Fan, Xiaojin Zhang, Wei Chen, Xiang Bai

Text-to-SQL generation aims to translate natural language questions into SQL statements.

Text-To-SQL

MC-CoT: A Modular Collaborative CoT Framework for Zero-shot Medical-VQA with LLM and MLLM Integration

1 code implementation6 Oct 2024 Lai Wei, Wenkai Wang, Xiaoyu Shen, Yu Xie, Zhihao Fan, Xiaojin Zhang, Zhongyu Wei, Wei Chen

In recent advancements, multimodal large language models (MLLMs) have been fine-tuned on specific medical image datasets to address medical visual question answering (Med-VQA) tasks.

Medical Visual Question Answering Question Answering +1

Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory

no code implementations23 Jul 2024 Xiaojin Zhang, Wei Chen

From the optimization theory perspective, we establish an upper bound on the privacy leakage in terms of the batch size, the distortion extent, and several other factors.

Federated Learning Privacy Preserving

A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning

no code implementations5 Jul 2024 Xiaojin Zhang, Mingcong Xu, Wei Chen

In this paper, we first give an introduction to the theoretical basis of the privacy-utility equilibrium in federated learning based on Bayesian privacy definitions and total variation distance privacy definitions.

Federated Learning Navigate +1

VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models

1 code implementation11 Jun 2024 Yu Liu, Lang Gao, Mingxin Yang, Yu Xie, Ping Chen, Xiaojin Zhang, Wei Chen

However, sound comprehensive research on detecting program vulnerabilities, a more specific task related to code, and evaluating the performance of LLMs in this more specialized scenario is still lacking.

Vulnerability Detection

No Free Lunch Theorem for Privacy-Preserving LLM Inference

no code implementations31 May 2024 Xiaojin Zhang, Yulin Fei, Yan Kang, Wei Chen, Lixin Fan, Hai Jin, Qiang Yang

Therefore, it is essential to evaluate the balance between the risk of privacy leakage and loss of utility when conducting effective protection mechanisms.

Privacy Preserving

Deciphering the Interplay between Local Differential Privacy, Average Bayesian Privacy, and Maximum Bayesian Privacy

no code implementations25 Mar 2024 Xiaojin Zhang, Yulin Fei, Wei Chen

The swift evolution of machine learning has led to emergence of various definitions of privacy due to the threats it poses to privacy, including the concept of local differential privacy (LDP).

Privacy Preserving

Reinforcement Learning as a Catalyst for Robust and Fair Federated Learning: Deciphering the Dynamics of Client Contributions

1 code implementation8 Feb 2024 Jialuo He, Wei Chen, Xiaojin Zhang

Recent advancements in federated learning (FL) have produced models that retain user privacy by training across multiple decentralized devices or systems holding local data samples.

continuous-control Continuous Control +3

CauESC: A Causal Aware Model for Emotional Support Conversation

no code implementations31 Jan 2024 Wei Chen, Hengxu Lin, Qun Zhang, Xiaojin Zhang, Xiang Bai, Xuanjing Huang, Zhongyu Wei

Emotional Support Conversation aims at reducing the seeker's emotional distress through supportive response.

K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning

no code implementations16 Dec 2023 Wei Chen, Gang Zhao, Xiaojin Zhang, Xiang Bai, Xuanjing Huang, Zhongyu Wei

Automatic psychological counseling requires mass of professional knowledge that can be found in online counseling forums.

Diversity Response Generation

Grounding Foundation Models through Federated Transfer Learning: A General Framework

no code implementations29 Nov 2023 Yan Kang, Tao Fan, Hanlin Gu, Xiaojin Zhang, Lixin Fan, Qiang Yang

Motivated by the strong growth in FTL-FM research and the potential impact of FTL-FM on industrial applications, we propose an FTL-FM framework that formulates problems of grounding FMs in the federated learning setting, construct a detailed taxonomy based on the FTL-FM framework to categorize state-of-the-art FTL-FM works, and comprehensively overview FTL-FM works based on the proposed taxonomy.

Federated Learning Privacy Preserving +1

Privacy in Large Language Models: Attacks, Defenses and Future Directions

no code implementations16 Oct 2023 Haoran Li, Yulin Chen, Jinglong Luo, Jiecong Wang, Hao Peng, Yan Kang, Xiaojin Zhang, Qi Hu, Chunkit Chan, Zenglin Xu, Bryan Hooi, Yangqiu Song

The advancement of large language models (LLMs) has significantly enhanced the ability to effectively tackle various downstream NLP tasks and unify these tasks into generative pipelines.

A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning

no code implementations28 May 2023 Xiaojin Zhang, Yan Kang, Lixin Fan, Kai Chen, Qiang Yang

Motivated by this requirement, we propose a framework that (1) formulates TFL as a problem of finding a protection mechanism to optimize the tradeoff between privacy leakage, utility loss, and efficiency reduction and (2) formally defines bounded measurements of the three factors.

Federated Learning Meta-Learning

Theoretically Principled Federated Learning for Balancing Privacy and Utility

no code implementations24 May 2023 Xiaojin Zhang, Wenjie Li, Kai Chen, Shutao Xia, Qiang Yang

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 utility.

Federated Learning

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion

no code implementations7 May 2023 Xiaojin Zhang, Kai Chen, Qiang Yang

The nature of the widely-adopted protection mechanisms including \textit{Randomization Mechanism} and \textit{Compression Mechanism} is to protect privacy via distorting model parameter.

Federated Learning Privacy Preserving

A Game-theoretic Framework for Privacy-preserving Federated Learning

no code implementations11 Apr 2023 Xiaojin Zhang, Lixin Fan, Siwei Wang, Wenjie Li, Kai Chen, Qiang Yang

To address this, we propose the first game-theoretic framework that considers both FL defenders and attackers in terms of their respective payoffs, which include computational costs, FL model utilities, and privacy leakage risks.

Federated Learning Privacy Preserving

Probably Approximately Correct Federated Learning

no code implementations10 Apr 2023 Xiaojin Zhang, Anbu Huang, Lixin Fan, Kai Chen, Qiang Yang

However, existing multi-objective optimization frameworks are very time-consuming, and do not guarantee the existence of the Pareto frontier, this motivates us to seek a solution to transform the multi-objective problem into a single-objective problem because it is more efficient and easier to be solved.

Federated Learning PAC learning

A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning

1 code implementation8 Sep 2022 Yan Kang, Jiahuan Luo, Yuanqin He, Xiaojin Zhang, Lixin Fan, Qiang Yang

We then use this framework as a guide to comprehensively evaluate a broad range of protection mechanisms against most of the state-of-the-art privacy attacks for three widely deployed VFL algorithms.

Privacy Preserving Vertical Federated Learning

Trading Off Privacy, Utility and Efficiency in Federated Learning

no code implementations1 Sep 2022 Xiaojin Zhang, Yan Kang, Kai Chen, Lixin Fan, Qiang Yang

In addition, it is a mandate for a federated learning system to achieve high \textit{efficiency} in order to enable large-scale model training and deployment.

Vertical Federated Learning

No Free Lunch Theorem for Security and Utility in Federated Learning

no code implementations11 Mar 2022 Xiaojin Zhang, Hanlin Gu, Lixin Fan, Kai Chen, Qiang Yang

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.

Federated Learning Privacy Preserving

Variance-Dependent Best Arm Identification

no code implementations19 Jun 2021 Pinyan Lu, Chao Tao, Xiaojin Zhang

Given a set of $n$ arms indexed from $1$ to $n$, each arm $i$ is associated with an unknown reward distribution supported on $[0, 1]$ with mean $\theta_i$ and variance $\sigma_i^2$.

Adaptive Double-Exploration Tradeoff for Outlier Detection

no code implementations13 May 2020 Xiaojin Zhang, Honglei Zhuang, Shengyu Zhang, Yuan Zhou

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.

Outlier Detection

Contextual Combinatorial Conservative Bandits

no code implementations26 Nov 2019 Xiaojin Zhang, Shuai Li, Weiwen Liu, Shengyu Zhang

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 of practical scenarios.

Multi-Armed Bandits

Automatic Ensemble Learning for Online Influence Maximization

no code implementations25 Nov 2019 Xiaojin Zhang

Experimental evaluation illustrates the effectiveness of the automatically adjusted hybridization of exploration algorithm with exploitation algorithm.

Ensemble Learning Thompson Sampling

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