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arXiv 2023年

作者： Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Li, Minghui Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of robustness. In this paper, we present a novel poisoning attack known as MISA. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention. Copyright © 2023, The Authors. All rights reserved.

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Towards high-throughput and low-latency billion-scale vector search via CPU/GPU collaborative filtering and re-ranking 25

Towards high-throughput and low-latency billion-scale vector...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Bing Tian Haikun Liu Yuhang Tang Shihai Xiao Zhuohui Duan Xiaofei Liao Hai Jin Xuecang Zhang Junhua Zhu Yu Zhang National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd Towards high-throughput and low-latency billion-scale vector search via CPU/GPU collaborative filtering and re-ranking

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present Fusion-ANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of Fusion-ANNS lies in CPU/GPU collaborative filtering and reranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN; 2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy.

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

Robin: A Novel Method to Produce Robust Interpreters for Dee...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Zhen Li Ruqian Zhang Deqing Zou Ning Wang Yating Li Shouhuai Xu Chen Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security Department of Computer Science University of Colorado Colorado Springs USA Center for Research in Computer Vision University of Central Florida USA School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA.

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Jinan250353 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning

Challenges and Approaches for Mitigating Byzantine Attacks i...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Junyu Shi Wei Wan Shengshan Hu Jianrong Lu Leo Yu Zhang School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Information Technology Deakin University

Recently emerged federated learning (FL) is an attractive distributed learning framework in which numerous wireless end-user devices can train a global model with the data remained autochthonous. Compared with the traditional machine learning framework that collects user data for centralized storage, which brings huge communication burden and concerns about data privacy, this approach can not only save the network bandwidth but also protect the data privacy. Despite the promising prospect, Byzantine attack, an intractable threat in conventional distributed network, is discovered to be rather efficacious against FL as well. In this paper, we conduct a comprehensive investigation of the state-of-the-art strategies for defending against Byzantine attacks in FL. We first provide a taxonomy for the existing defense solutions according to the techniques they used, followed by an across-the-board comparison and discussion. Then we propose a new Byzantine attack method called weight attack to defeat those defense schemes, and conduct experiments to demonstrate its threat. The results show that existing defense solutions, although abundant, are still far from fully protecting FL. Finally, we indicate possible countermeasures for weight attack, and highlight several challenges and future research directions for mitigating Byzantine attacks in FL.

关键词： Wireless communication Training Privacy data privacy Federated learning Distance learning Taxonomy

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HLMEA: Unsupervised Entity Alignment Based on Hybrid Language Models 39

HLMEA: Unsupervised Entity Alignment Based on Hybrid Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jin, Xiongnan Wang, Zhilin Chen, Jinpeng Yang, Liu Oh, Byungkook Hwang, Seung-Won Li, Jianqiang National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Alibaba Group China School of Computer Science Beijing University of Posts and Telecommunications China Xiangjiang Laboratory China School of Computer Science and Engineering Central South University China Computer Science & Engineering Konkuk University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of

ISBN: (纸本)157735897X

Entity alignment (EA) is crucial for integrating knowledge graphs (KGs) constructed from diverse sources. Conventional unsupervised EA approaches attempt to eliminate human intervention but often suffer from accuracy limitations. With the rise of large language models (LLMs), leveraging their capabilities for EA presents a promising direction. However, it introduces new challenges: formulating the LLM-based EA problem and extracting the background knowledge in LLMs to realize EA without human intervention. This paper proposes HLMEA, a novel hybrid language model-based unsupervised EA method. HLMEA formulates the EA task into a filtering and single-choice problem and synergistically integrates small language models (SLMs) and LLMs. Specifically, SLMs filter candidate entities based on textual representations generated from KG triples. Then, LLMs refine this selection to identify the most semantically aligned entities. An iterative self-training mechanism allows SLMs to distill knowledge from LLM outputs, enhancing the EA ability of hybrid language models in subsequent rounds cooperatively. We also conducted extensive experiments on benchmark datasets to evaluate HLMEA's performance. The results demonstrate that HLMEA significantly outperforms unsupervised and even supervised EA baselines, proving its potential for scalable and effective EA across large KGs. The code and data are available at https://***/xnjin-ai/HLMEA. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Graph-Augmented Contrastive Clustering for Time Series

SSRN

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SSRN 2023年

作者： Zhang, Qin Liang, Zhuoluo Ngueilbaye, Alladoumbaye Zhang, Peng Chen, Junyang Chen, Xiaojun Huang, Joshua Zhexue College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China

The recent emergence of time series contrastive clustering methods can be categorized into two classes. The first class uses contrastive learning for universal representations, which can be effective in various downstream tasks, but overlooks important categorical information and clustering objectives, leading to unsuitable models. The second class incorporates contrastive learning into clustering objectives, but fails to fully explore potential connections and structures between time series data. To this end, we propose the "Time Series Graph-augmented Contrastive Clustering (TSGCC) method", a graph-augmented contrastive learning framework aiming to create cluster-friendly features and assignments by iteratively optimizing contrastive loss. The method uses a weighted KNN graph to build positive and negative sample pairs, projecting the instances into a feature space with the dimensionality number of clusters. Experimental results demonstrate that TSGCC outperforms 18 advanced time-series clustering methods on the 36 representatives out of the 128 UCR benchmarks, achieving the best clustering results on 12 datasets, with the highest average rank (3.22) of RI and the second-highest average rank (3.89) of NMI among 18 clustering methods. We further compared our method with recent approaches on the entire set of 128 UCR datasets, demonstrating our method's broad applicability and effectiveness. © 2023, The Authors. All rights reserved.

关键词： Time series

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EPL: Empirical Prototype Learning for Deep Face Recognition

arXiv

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arXiv 2024年

作者： Fan, Weijia Wen, Jiajun Jia, Xi Shen, Linlin Zhou, Jiancan Li, Qiufu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Computer Vision Institute Shenzhen University Shenzhen518060 China School of Computer Science University of Birmingham Birmingham United Kingdom Aqara Lumi United Technology Coopration Shenzhen China

Prototype learning is widely used in face recognition, which takes the row vectors of coefficient matrix in the last linear layer of the feature extraction model as the prototypes for each class. When the prototypes are updated using the facial sample feature gradients in the model training, they are prone to being pulled away from the class center by the hard samples, resulting in decreased overall model performance. In this paper, we explicitly define prototypes as the expectations of sample features in each class and design the empirical prototypes using the existing samples in the dataset. We then devise a strategy to adaptively update these empirical prototypes during the model training based on the similarity between the sample features and the empirical prototypes. Furthermore, we propose an empirical prototype learning (EPL) method, which utilizes an adaptive margin parameter with respect to sample features. EPL assigns larger margins to the normal samples and smaller margins to the hard samples, allowing the learned empirical prototypes to better reflect the class center dominated by the normal samples and finally pull the hard samples towards the empirical prototypes through the learning. The extensive experiments on MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace demonstrate the effectiveness of EPL. Our code is available at https://***/WakingHours-GitHub/EPL. © 2024, CC BY.

关键词： Face recognition

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Query-Centered Temporal Community Search via Time-Constrained Personalized PageRank

arXiv

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arXiv 2023年

作者： Lin, Longlong Yuan, Pingpeng Li, Rong-Hua Zhu, Chunxue Qin, Hongchao Jin, Hai Jia, Tao College of Computer and Information Science Southwest University Chongqing400715 China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technololgy Beijing Institute of Technology Beijing100081 China

Existing temporal community search suffers from two defects: (i) they ignore the temporal proximity between the query vertex q and other vertices but simply require the result to include q. Thus, they find many temporal irrelevant vertices (these vertices are called query-drifted vertices) to q for satisfying their cohesiveness, resulting in q being marginalized;(ii) their methods are NP-hard, incurring high costs for exact solutions or compromised qualities for approximate/heuristic algorithms. Inspired by these, we propose a novel problem named query-centered temporal community search to circumvent query-drifted vertices. Specifically, we first present a novel concept of Time-Constrained Personalized PageRank to characterize the temporal proximity between q and other vertices. Then, we introduce a model called β-temporal proximity core, which can combine temporal proximity and structural cohesiveness. Subsequently, our problem is formulated as an optimization task that finds a β-temporal proximity core with the largest β. To solve our problem, we first devise an exact and near-linear time greedy removing algorithm that iteratively removes unpromising vertices. To improve efficiency, we then design an approximate two-stage local search algorithm with bound-based pruning techniques. Finally, extensive experiments on eight real-life datasets and nine competitors show the superiority of the proposed solutions. © 2023, CC BY.

关键词： Iterative methods

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Research Landscapes, Hotspots and Emerging Frontiers of Combinatorial Optimization Algorithms on Graphs: A Scientometric Analysis (1987 - 2022)

Research Landscapes, Hotspots and Emerging Frontiers of Comb...

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International Conference on big data and Information Analytics (bigDIA)

作者： Boliang Sun Hai Chen Ziqi You Laboratory for Big Data and Decision National University of Defense Technology ChangSha People’s Republic of China College of System Engineering National University of Defense Technology ChangSha People’s Republic of China

This paper presents a scientometric analysis of the research landscapes, hotspots, and emerging frontiers of combinatorial optimization algorithms on graphs from 1987 to 2022. The study investigates technical maturity, country/region collaboration, institutional cooperation, journal co-citation, dual graph overlay, and hotspot frontier analysis based on co-citation and keyword co-occurrence using tools like CiteSpace, Bibliometrics R, and VOSviewer. The analysis reveals that research in this field has entered a mature stage, and countries such as the United States, China, and Japan have produced rich output in this field. The University of Tokyo, MIT, the Chinese Academy of Sciences, Shanghai Jiao Tong University, Stanford University, and the University of California have formed 10 distinct collaborative clusters. The findings also suggest that research in the field of combination optimization mainly comes from two clusters, namely, computer science and mathematics, and that cross-disciplinary knowledge is also involved. In the field of graph-based combination optimization, deep learning, machine learning, graph neural networks, and reinforcement learning are now areas of intense investigation. The frontier direction of research in this field is represented by quantum computing technologies such as coherent ising machines, hybrid quantum-classical algorithms, and quantum algorithms. The research problems in this field are categorized into three main types from a keyword perspective: graph theory problems, classical optimization problems, and open problems. The corresponding methods for solving these problems are also categorized into combinatorial optimization techniques, stochastic optimization methods, and machine learning-based optimization methods. The study highlights the need for interdisciplinary collaboration, more research on quantum computing technologies, advanced optimization techniques, and the exploration of emerging research frontiers in combination o

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