Search Results for author: Shui Yu

Found 21 papers, 3 papers with code

DDFAD: Dataset Distillation Framework for Audio Data

no code implementations15 Jul 2024 Wenbo Jiang, Rui Zhang, Hongwei Li, Xiaoyuan Liu, Haomiao Yang, Shui Yu

The remarkable performance of DNNs is largely attributed to the availability of massive, high-quality training datasets.

Continual Learning Dataset Distillation +1

Explainable AI Security: Exploring Robustness of Graph Neural Networks to Adversarial Attacks

no code implementations20 Jun 2024 Tao Wu, Canyixing Cui, Xingping Xian, Shaojie Qiao, Chao Wang, Lin Yuan, Shui Yu

Graph neural networks (GNNs) have achieved tremendous success, but recent studies have shown that GNNs are vulnerable to adversarial attacks, which significantly hinders their use in safety-critical scenarios.

Adversarial Robustness

Inference Attacks: A Taxonomy, Survey, and Promising Directions

no code implementations4 Jun 2024 Feng Wu, Lei Cui, Shaowen Yao, Shui Yu

The prosperity of machine learning has also brought people's concerns about data privacy.

Inference Attack

Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach

no code implementations27 May 2024 Xuhan Zuo, Minghao Wang, Tianqing Zhu, Lefeng Zhang, Shui Yu, Wanlei Zhou

With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative.

Federated Learning Machine Unlearning +1

Machine Unlearning: A Comprehensive Survey

no code implementations13 May 2024 Weiqi Wang, Zhiyi Tian, Shui Yu

We categorize current unlearning methods into four scenarios: centralized unlearning, distributed and irregular data unlearning, unlearning verification, and privacy and security issues in unlearning.

Machine Unlearning

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks

1 code implementation11 Dec 2023 Yuyang Zhou, Guang Cheng, Zongyao Chen, Shui Yu

Experimental results on two Android malware datasets demonstrate that MalPurifier outperforms the state-of-the-art defenses, and it significantly strengthens the vulnerable malware detector against 37 evasion attacks, achieving accuracies over 90. 91%.

Adversarial Purification Android Malware Detection +3

Towards Blockchain-Assisted Privacy-Aware Data Sharing For Edge Intelligence: A Smart Healthcare Perspective

no code implementations29 Jun 2023 Youyang Qu, Lichuan Ma, Wenjie Ye, Xuemeng Zhai, Shui Yu, Yunfeng Li, David Smith

Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining.

Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System

no code implementations30 May 2023 Jiwei Guan, Lei Pan, Chen Wang, Shui Yu, Longxiang Gao, Xi Zheng

As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios.

Autonomous Driving Open-Ended Question Answering +1

pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning

1 code implementation25 May 2023 Jiahao Tan, Yipeng Zhou, Gang Liu, Jessie Hui Wang, Shui Yu

More specifically, we decouple a NN model into a personalized feature extractor, obtained by aggregating models from similar clients, and a classifier, which is obtained by local training and used to estimate client similarity.

Personalized Federated Learning

Social-Aware Clustered Federated Learning with Customized Privacy Preservation

no code implementations25 Dec 2022 Yuntao Wang, Zhou Su, Yanghe Pan, Tom H Luan, Ruidong Li, Shui Yu

In this paper, we strike the balance of data privacy and efficiency by utilizing the pervasive social connections between users.

Federated Learning

Machine Learning Empowered Intelligent Data Center Networking: A Survey

no code implementations28 Feb 2022 Bo Li, Ting Wang, Peng Yang, Mingsong Chen, Shui Yu, Mounir Hamdi

To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization.

BIG-bench Machine Learning Management

Efficient Federated Learning for AIoT Applications Using Knowledge Distillation

no code implementations29 Nov 2021 Tian Liu, Zhiwei Ling, Jun Xia, Xin Fu, Shui Yu, Mingsong Chen

Inspired by Knowledge Distillation (KD) that can increase the model accuracy, our approach adds the soft targets used by KD to the FL model training, which occupies negligible network resources.

Federated Learning Knowledge Distillation

Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacy

1 code implementation5 Jul 2021 Yipeng Zhou, Xuezheng Liu, Yao Fu, Di wu, Chao Li, Shui Yu

In this work, we study a crucial question which has been vastly overlooked by existing works: what are the optimal numbers of queries and replies in FL with DP so that the final model accuracy is maximized.

Federated Learning

Variational Co-embedding Learning for Attributed Network Clustering

no code implementations15 Apr 2021 Shuiqiao Yang, Sunny Verma, Borui Cai, Jiaojiao Jiang, Kun Yu, Fang Chen, Shui Yu

Recent works for attributed network clustering utilize graph convolution to obtain node embeddings and simultaneously perform clustering assignments on the embedding space.

Attribute Clustering +2

On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times

no code implementations11 Jan 2021 Yao Fu, Yipeng Zhou, Di wu, Shui Yu, Yonggang Wen, Chao Li

Then, we theoretically derive: 1) the conditions for the DP based FedAvg to converge as the number of global iterations (GI) approaches infinity; 2) the method to set the number of local iterations (LI) to minimize the negative influence of DP noises.

Federated Learning

Improving robustness of softmax corss-entropy loss via inference information

no code implementations1 Jan 2021 Bingbing Song, wei he, Renyang Liu, Shui Yu, Ruxin Wang, Mingming Gong, Tongliang Liu, Wei Zhou

Several state-of-the-arts start from improving the inter-class separability of training samples by modifying loss functions, where we argue that the adversarial samples are ignored and thus limited robustness to adversarial attacks is resulted.

Detecting Adversarial Examples by Additional Evidence from Noise Domain

no code implementations1 Jan 2021 Song Gao, Shui Yu, Shaowen Yao

In this paper, we utilize the steganalysis rich model (SRM) to generate noise feature maps, and combine them with RGB images to discover the difference between adversarial examples and clean examples.


Can Steering Wheel Detect Your Driving Fatigue?

no code implementations18 Oct 2020 Jianchao Lu, Xi Zheng, Tianyi Zhang, Michael Sheng, Chen Wang, Jiong Jin, Shui Yu, Wanlei Zhou

In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel.

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis

no code implementations25 Apr 2020 Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu

Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area.

Anomaly Detection Organ Segmentation +1

Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems

no code implementations1 Jun 2019 Binbin Jin, Enhong Chen, Hongke Zhao, Zhenya Huang, Qi Liu, HengShu Zhu, Shui Yu

Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A), where the multi-facet domain effects in CQA are still underexplored.

Answer Selection Community Question Answering +1

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