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TraCemop: Towards Federated Learning with Traceable Contribution Evaluation and Model Ownership Protection

IEEE Transactions on Mobile computing 2025年

作者： Liu, Jingwei Zhou, Zihan Sun, Rong Liu, Lei Lu, Rongxing Dustdar, Schahram Niyato, Dusit Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an 710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an 710071 China Xidian University Guangzhou Institute of Technology Guangzhou 510555 China Qilu University of Technology (Shandong Academy of Sciences) Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan 250014 China Queen's University School of Computing Kingston K7L 2N8 ON Canada TU Wien Distributed Systems Group Austria UPF ICREA Barcelona Spain Nanyang Technological University School of Computer Science and Engineering Singapore

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership. © 2002-2012 IEEE.

关键词： Blockchain Contribution evaluation Federated learning Model IPR Watermark

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Visual malware classification and detection method based on convolutional neural network 9

Visual malware classification and detection method based on ...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Huang, Hua Sheng, Hua Han, Xingwang Zhang, Wenbin Zhao, Zilin State Grid Shandong Electric Power Company Information and Telecommunications Company Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

With the rapid development of Internet technology, leading to the evolution of malware variants, and malicious threats are also increasing. Therefore, rapid and accurate classification of malware becomes crucial. In response to this challenge, this paper proposes a malware classification method based on deep learning and visualization techniques. The method first extracts sample features, then converts malware binary samples into image format, thus transforming abstract binary data into images with visual information. Subsequently, color mapping technique is applied to convert the processed images into color images. Furthermore, to address imbalanced datasets, this paper introduces data augmentation methods to ensure more balanced and comprehensive training of the model across different sample categories. Finally, an improved convolutional neural network architecture is employed for the classification detection of malware families, achieving accurate classification and identification of different malware families through learning and analysis of sample features. To evaluate the effectiveness of the proposed method, experiments are conducted on the Google Code Jam (GCJ) benchmark dataset. The experimental results demonstrate an accuracy rate of 99.29% on the GCJ dataset. Additionally, we also compared the performance of the improved method before and after, and found that the proposed method can provide more comprehensive feature representation and significantly improve classification accuracy and detection efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Discourse Relation-Aware Multi-turn Dialogue Response Generation 12th

Discourse Relation-Aware Multi-turn Dialogue Response Genera...

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12th National CCF Conference on Natural Language Processing and Chinese computing, NLPCC 2023

作者： Wang, Huijie He, Ruifang Jia, Yungang Xu, Jing Wang, Bo College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Branch of National Computer Network and Information Security Management Center Tianjin China

ISBN: (纸本)9783031446924

Multi-turn dialogue response generation aims to generate a response with consideration of the context. It is not equal to multiple single-turn dialogues due to the context dependence of response. Many existing models achieve great success for response generation, but they still struggle to model the contextual semantics of dialogue history. Sequence models have difficulties to explore the interactive relations between contextual utterances, which affects the coherence of generated responses. To solve the issue, we propose a discourse relation-aware model, which encodes the contextual utterances with a directed acyclic graph neural network (DAGNN) with constraints on dialogue-specific discourse relations to better model the intrinsic structure. Besides, we introduce an auxiliary discourse relation recognition task to enhance the model’s ability of representing the context. Extensive experimental results show that our proposed model outperforms baselines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Directed graphs

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BEW-YOLO: An Improved Method for PCB Defect Detection Based on YOLOv7 29

BEW-YOLO: An Improved Method for PCB Defect Detection Based ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Li, Zhiyao Li, Aimin Zhang, Yuechen Kong, Xiaotong Li, Wenqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350330717

The PCB defect object size is small and the detection process is susceptible to background interference, usually have the problem of missed and false detection. In order to solve the above problems, an improved method based on YOLOv7 is proposed in this paper. Firstly, the bi-level routing attention (BRA) has been added to the header of the original YOLOv7 model to capture global dependencies and ensure the accuracy of small object detection and localization. Secondly, the explicit visual center (EVC) block is introduced before the fusion of mid-level features and high-level features to capture the global remote dependencies of top-level features, extract the features in local corner regions, achieve a comprehensive feature representation. Finally, the loss function is improved by changing the loss function of the original model to Wise-IoU, which allows our model to focus more on ordinary-quality anchor boxes and improve the performance of the detector while decreasing the competitiveness of anchor boxes for high-quality samples and reducing the influence of low-quality samples on the detection results. The experimental results show that the improved model improves 5% in AP and 1.9% in both AR and mAP over the original YOLOv7 model. Meanwhile, comparison experiments are conducted on the data-enhanced PCB dataset, which proves the superiority of our model over other state-of-the-art models. © 2023 IEEE.

关键词： attention mechanism defect detection PCB YOLOv7

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Prompt-Based Few-Shot Text Classification with Virtual Class Representation 9

Prompt-Based Few-Shot Text Classification with Virtual Class...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Ju, Zhengyu Li, Zhao Li, Ye Wu, Shiwei Wang, Canjun Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Jinan China Jinan China Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Fundamental Research Center for Computer Science Jinan China Evay Info Jinan China

ISBN: (纸本)9798350317251

Over the past few years, "Pre-training + Fine-tuning"has been highly successful in Natural Language Processing (NLP) tasks, but it has limitations in few-shot tasks and zero-shot tasks. Recently, "Prompt-based Tuning + Pretrained Language models (PLMs)"has achieved great success in few-shot stage and zero-shot stage, including few-shot text classification. Prompt-based tuning wraps suggestive characters and [MASK] tokens into a raw input sequence. Additionally, Prompt learning uses a verbalizer to map the semantic information of the [MASK] position to the final classification prediction. The verbalizer contains manual verbalizer and automatic verbalizer, etc. However, manual verbalizers are too dependent on human experience and external knowledge, while automatic verbalizers still face significant challenges. In this work, we introduce a novel approach for constructing a verbalizer termed as 'virtual class representation'. This method entails assigning a fixed-length sequence of digits to represent each label in the dataset. We train a small-scale randomly initialized embedding mechanism to achieve preliminary semantic information for each class when virtual class representation is introduced into the embedding mechanism. The semantic information of each category is then further extracted by a convolution operation, and the final model regression value is obtained by calculating the similarity between the last hidden state of PLM and the semantic vector of the class. Our extensive experiments demonstrate the effectiveness of the proposed method on few-shot text classification tasks. © 2023 IEEE.

关键词： Embeddings

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A design of the common platform of wireless communication based on FPGA

A design of the common platform of wireless communication ba...

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2017 IEEE Conference on Energy Internet and Energy System Integration, EI2 2017

作者： Qiao, Lei Zheng, Zhe Cui, Wenpeng Zhang, Weilong Jiang, Xueping Research Department of Smart Chip Global Energy Interconnection Research Institute Beijing China State Grid Key Laboratory of Information and Network Security State Grid Laboratory of Electric Power Communication Network Technology Beijing China

ISBN: (纸本)9781538614273

We present a design of the common platform of wireless communication based on FPGA. The platform, developed by us, allows researchers to modify transport protocols on any layer of an OSI reference model, from physical layer to application one, thereby it is feasible to implement multiple wireless communication protocols on different layers. © 2017 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Multiple influence maximization in social networks 10

Multiple influence maximization in social networks

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10th International Conference on Ubiquitous information Management and Communication, IMCOM 2016

作者： Sun, Honglin Gao, Xiaofeng Chen, Guihai Gu, Jian Wang, Yongjian Shanghai Key Lab. of Scalable Computing and Systems Dept. of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of Information Network Security of Ministry of Public Security Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9781450341424

Influence Maximization, a technique of social analysis to help marketers select a small group of users to promote their products with the goal of maximizing their influence spread, has been studied for a long time. However, previous researches did not consider the scenario that a company may intend to promote serval kinds of products in the same social network, where each user has different preferences for different categories. Considering this scenario in this paper, we propose the Multiple Influence Maximization (MIM) problem based on the assumption that one seed user can accept serval kinds of goods for free, and at the same time non-seed users have enough purchasing power to accept different promotions from their social friends. To address this issue, we design the corresponding greedy algorithm framework with an approximate radio of 1-1/e if the influence function is submodular. Besides, to simplify the computation of influence spread and considering the properties of real social influence propagation, we propose a novel influence quantitative computation model in which influence function fortunately is submodular. Applying this model into our MIM problem, the experiment results show that the greedy algorithm out-performs serval traditional heuristics in influence spread without loss of performance. © 2015 ACM.

关键词： Method of moments

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Edge-Based Resilient Consensus Control of Islanded Microgrids 1

Edge-Based Resilient Consensus Control of Islanded Microgrid...

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1st International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2022

作者： Jiang, Yixin Xu, Peiming Xu, Wenqian Liang, Zhihong Yang, Yiwei Zhang, Yunan Huang, Kaitian Zhai, Meina Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China Northeastern University College of Information Science and Engineering Shenyang110819 China

ISBN: (纸本)9781665452151

We present a resilient output voltage recovery strategy for AC microgrids (MGs). In the presence of network attacks, distributed generators (DGs) may experience partial loss of effectiveness (PLOE) and bias faults in their actuators. These fault parameters can be heterogeneous and time-varying, posing significant challenges to voltage regulation. To address this, our approach incorporates adaptive techniques to handle the fault parameters while avoiding their direct dependence. Furthermore, we utilize boundary layers to design smooth controllers that mitigate the adverse effects of non-smooth controllers, ensuring stable and precise voltage regulation. Through extensive simulations, we validate the effectiveness of our proposed strategy in achieving reliable and robust load voltage regulation in AC MGs. © 2023 IEEE.

关键词： Multi agent systems

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LMS Channel Estimation Algorithm for IM/DD-OFDM/OQAM-PON Systems in 5G-Enabled Industrial Internet

LMS Channel Estimation Algorithm for IM/DD-OFDM/OQAM-PON Sys...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Liang, Siyuan Wang, Xuefen Du, Jianbo Zhao, Feng Cao, Haotong Xi'An University of Posts and Telecommunications Shanxi Key Laboratory of Information Communication Network and Security Xi'an China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (纸本)9781665423908

Orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) passive optical network (PON) is an attractive technique for 5G-enabled Industrial Internet due to the large bandwidth and ultra-low latency. However, In the OFDM/OQAM-PON system, the intrinsic imaginary interference(IMI) induced by the chromatic dispersion (CD) and polarization mode dispersion (PMD) undermines the system performance. In this paper, we proposed least mean square channel estimation(LMS-CE) algorithm, which not only effectively improves channel estimation performance, but also mitigates the IMI effect. Simulation results show that LMS-CE algorithm exhibits more than a 30% reduction in the bit error rate (BER) than the least squares (LS) algorithm. © 2021 IEEE.

关键词： Orthogonal frequency division multiplexing

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Policy network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud networks

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China Communications 2024年第4期21卷 53-73页

作者： Feng Chuan Zhang Xu Han Pengchao Ma Tianchun Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communication and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/***, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

关键词： benefit maximization deep reinforcement learning multi-access edge cloud task offloading

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