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1st Conference on Blockchain and Web3.0 technology Innovation and Application, BWTAC 2024

作者： Ni, Lina Song, Chenglin Zhao, Hanmo Tang, Yuncan Ma, Yunshen Zhang, Jinquan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Key Laboratory of the Ministry of Education for Embedded System and Service Computing Tongji University Shanghai201804 China

ISBN: (纸本)9789819794119

Federated Learning (FL), hailed as a potent approach in merging medical expertise, promises to elevate collaborative efforts among healthcare institutions while safeguarding the privacy and security of sensitive medical data, thereby energizing the trajectory of intelligent healthcare advancements. However, the diversity in training objectives across medical institutions can diminish their active participation in a unified federated training process. To address this issue of waning enthusiasm due to diverse training goals, in this paper, we propose Personalized Medical Federated Learning method based on Mutual Distillation Knowledge (pFedMKD). Specifically, we propose a similarity-based mutual distillation model selection algorithm that reduces interference among medical institutions with notably diverse data distributions. Moreover, we develop a server-side local model update method leveraging mutual knowledge distillation, enabling each local model to gain beneficial insights for personalization from analogous models. Additionally, we formulate a personalized model update method utilizing knowledge distillation from specifically labeled samples, thereby improving the performance of personalized models. Experimental results demonstrate that compared to the baseline algorithm, pFedMKD can more effectively address the target heterogeneity problem among different medical institutions, enabling each institution to achieve higher personalized performance in federated learning tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Fast and Scalable Gate-Level Simulation in Massively Parallel systems

Fast and Scalable Gate-Level Simulation in Massively Paralle...

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IEEE International Conference on Computer-Aided Design

作者： Haichuan Hu Zichen Xu Yuhao Wang Fangming Liu Services Computing Technology and System Lab Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Mathematics and Computer Science Nanchang University Nanchang China Pengcheng Laboratory Shenzhen China

The natural bijection between a proposed circuit design and its graph representation shall allow any graph optimization algorithm deploying into many-core systems efficiently. However, this process suffers from the exponentially growing overhead and heavy memory footprint with the signal propagation. To conquer the unique challenge, we systematically study the simulation with millions of gates, and identify that the processing complexity could grow exponentially from the signal inputs, the skewness of the computational graph stays. Thus, we present ZhouBi, a fast and scalable gate-level simulation framework to fully exploit the parallelism from many-core systems. ZhouBi contributes in threefolds, (I) a graph representation that colors gate-level netlists and identifies skew partitions based on the graph skewness; (II) A set of heuristic algorithms that picks opportunistic and conservative algorithms to accelerate the simulation; (III) A system facility that supports selective mapping between simulation and many-core, providing a tradeoff between the risk of concurrent simulation fail and performance gain. We have prototyped ZhouBi and evaluated it with practical baselines. ZhouBi can achieve a 27.6× performance gain, as compared to the state-of-the-practice Veriwell without compromising any correctness. Our framework supports large graphs enabling scale-out gate-level simulations for chip design.

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Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

arXiv

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arXiv 2024年

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

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six

关键词： Topology

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Adaptive Federated Learning Based on Device Performance in a Heterogeneous Environment of Medical computing Devices 1st

Adaptive Federated Learning Based on Device Performance in...

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1st Conference on Blockchain and Web3.0 technology Innovation and Application, BWTAC 2024

作者： Zhang, Jinquan Wang, Chongbo Yang, Rendong Tang, Yuncan Ma, Yunshen Ni, Lina College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Key Laboratory of the Ministry of Education for Embedded System and Service Computing Tongji University Shanghai201804 China

ISBN: (纸本)9789819794119

Federated Learning (FL), as one of the effective methods to solve the problem of medical data silos, can promote mutual cooperation among medical institutions under the premise of safeguarding the privacy and security of medical data, and effectively promote the development of intelligent medical treatment. However, the characteristics of medical data with multiple sources and heterogeneity bring certain challenges to the application of federated learning in the medical field. Due to the wide range of medical data sources, some medical institutions are unable to complete the federated learning training task on time due to the limitation of device performance, which leads to the failure of federated learning training. To address the problem of heterogeneity of computing devices among medical institutions that may cause some clients to fall behind, we propose an Adaptive Federated Learning based on Device Performance (AFedDP) method. Specifically, we design an adaptive local model training method to reduce the computational burden of medical institutions with poor equipment performance in the local model training process by adaptively constructing a local training model. Additionally, we propose a hierarchical model aggregation algorithm based on dynamic weight. This allows the heterogeneous adaptive local model to actively participate in the global model aggregation, making full use of the local data of each medical institution and improving the performance of the global model. Finally, we design an aggregation weight update algorithm based on momentum optimization to enhance the stability of global model aggregation. This algorithm updates contribution and aggregation weight with the idea of momentum optimization. Simulation experiments demonstrate that AFedDP achieves higher accuracy compared to the baseline algorithm. AFedDP can better address the issue of some devices being unable to participate in federated training properly. © The Author(s), under exclusive lic

关键词： Federated learning

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Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensional Input Spaces

Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensiona...

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International Conference on Software Engineering (ICSE)

作者： Haoxiang Jia Ming Wen Zifan Xie Xiaochen Guo Rongxin Wu Maolin Sun Kang Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab. Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System School of Informatics Xiamen University China School of Computer Science and Technology Huazhong University of Science and Technology China

Java Virtual Machine (JVM) is the fundamental software system that supports the interpretation and execution of Java bytecode. To support the surging performance demands for the increasingly complex and large-scale Java programs, Just-In-Time (JIT) compiler was proposed to perform sophisticated runtime optimization. However, this inevitably induces various bugs, which are becoming more pervasive over the decades and can often cause significant consequences. To facilitate the design of effective and efficient testing techniques to detect JIT compiler bugs. This study first performs a preliminary study aiming to understand the characteristics of JIT compiler bugs and the corresponding triggering test cases. Inspired by the empirical findings, we propose JOpFuzzer, a new JVM testing approach with a specific focus on JIT compiler bugs. The main novelty of JOpFuzzer is embodied in three aspects. First, besides generating new seeds, JOpFuzzer also searches for diverse configurations along the new dimension of optimization options. Second, JOpFuzzer learns the correlations between various code features and different optimization options to guide the process of seed mutation and option exploration. Third, it leverages the profile data, which can reveal the program execution information, to guide the fuzzing process. Such nov-elties enable JOpFuzzer to effectively and efficiently explore the two-dimensional input spaces. Extensive evaluation shows that JOpFuzzer outperforms the state-of-the-art approaches in terms of the achieved code coverages. More importantly, it has detected 41 bugs in OpenJDK, and 25 of them have already been confirmed or fixed by the corresponding developers.

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A Graph Reasoning Model Based on Multiple Types of Nodes 7

A Graph Reasoning Model Based on Multiple Types of Nodes

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7th International Conference on Big Data and Information Analytics, BigDIA 2021

作者： Liu, Yibing Huo, Lijun Zhou, Gang Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China National University of Defense Technology Key Laboratory of Information System Engineering Changsha China

ISBN: (纸本)9781665424660

Multi-hop machine reading comprehension (MRC) depends on the model to answer a semantically related question based on the given supporting text. In this study, inspired by previous research about Graph Neural Network(GNN), we develop a new graph covering three kinds of nodes: Sentence, Entity, Candidate. A corresponding MRC method based on graph inference is proposed, called SEC reasoner. The advantage of SECr is that it adopts different granular information as graph nodes, which can execute a more comprehensive reasoning. Our proposed model first initialize the node representations by co-Attention, multi-head attention and self-Attention based on context embedding. SECr then employs GNN based information transfer algorithms to collect evidences from the node encoding, and calculates a score for choosing the answer. In experiments, we evaluate the model on two popular benchmark datasets-WikiHop and MedHop. SECr reachs accuracy rates of 71.6%, 63.1% respectively, which is a competitive result. In addition, a comprehensive analysis proves the effectiveness and interpretability of the SECr. © 2021 IEEE.

关键词： Semantics

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

arXiv

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arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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Joint DNN Partition and Thread Allocation Optimization for Energy-Harvesting MEC systems

Joint DNN Partition and Thread Allocation Optimization for E...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Yizhou Shi Liying Li Yue Zeng Peijin Cong Junlong Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Deep neural networks (DNNs) have demonstrated exceptional performance, leading to diverse applications across various mobile devices (MDs). Considering factors like portability and environmental sustainability, an increasing number of MDs are adopting energy harvesting (EH) techniques for power supply. However, the computational intensity of DNNs presents significant challenges for their deployment on these resource-constrained devices. Existing approaches often employ DNN partition or offloading to mitigate the time and energy consumption associated with running DNNs on MDs. Nonetheless, existing methods frequently fall short in accurately modeling the execution time of DNNs, and do not consider to use thread allocation for further latency and energy consumption optimization. To solve these problems, we propose a dynamic DNN partition and thread allocation method to optimize the latency and energy consumption of running DNNs on EH-enabled MDs. Specifically, we first investigate the relationship between DNN inference latency and allocated threads and establish an accurate DNN latency prediction model. Based on the prediction model, a DRL-based DNN partition (DDP) algorithm is designed to find the optimal partitions for DNNs. A thread allocation (TA) algorithm is proposed to reduce the inference latency. Experimental results from our test-bed platform demonstrate that compared to four benchmarking methods, our scheme can reduce DNN inference latency and energy consumption by up to 37.3% and 38.5%.

关键词： Energy consumption Instruction sets Heuristic algorithms Artificial neural networks Predictive models Prediction algorithms Inference algorithms Partitioning algorithms Resource management Optimization

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

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International Conference on Software Engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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