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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'an710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Queen's University School of Computing KingstonONK7L 2N8 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： Federated learning

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Mobile Robot Object Recognition in The Internet of Things based on Fog computing

Mobile Robot Object Recognition in The Internet of Things ba...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Meixia Fu Songlin Sun Kaili Ni Xiaoying Hou National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service (BUPT)

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Mobile robot object recognition has attracted significant attention in the internet of things recently, in which there are many challenging tasks, such as the objects, the communication networks and the computer system. It still needs a large of computation, communication and storage capability for the whole system. In this paper, we propose a scheme of mobile robot object recognition in IOT and use edge nodes to process the data from robot vision instead of cloud computing. Besides, we adopt YOLOv3 as the main algorithm in the edge nodes to process the video data. The video from the camera on the robot is transmitted to the fog node by a Wi-Fi router. The advantages of using edge nodes for computation local robot clusters are reliability, real-time capability and flexibility compared with the cloud. In our experiment, we efficiently train the computer model using COCO database deployed to GPU. The robot using the proposed model could recognize the objects in real-time. We achieve mAP of 31.0% and response time of 52ms that illustrates the state-of-art performance of the proposed framework.

关键词： Object recognition Edge computing Cloud computing Real-time systems Servers Mobile robots

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Modulation Recognition based on Incremental Deep Learning

Modulation Recognition based on Incremental Deep Learning

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International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Yong Yang Menghan Chen XiaoYa Wang Piming Ma The 54th Research Institute of CETC Shijiazhuang China School of Information Science and Engineering Shandong University Qingdao China School of Information and Communication Engineering National Engineering Laboratory for Mobile Network Security Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications line Beijing China

Incremental learning has emerged to solve the problem of incrementally updating the classification model as the number of data classes grows. There are many challenges in incremental learning such as catastrophic forgetting and learning efficiency. In this paper, we present a method of the modulation recognition based on incremental learning, that allows learning continuously with a class-incremental way. The new classes can be added into the existing model progressively from a sequential data stream. We conduct experiments on the modulation signal dataset characterized by the constellation diagram, the experimental results prove the feasibility of our incremental learning system. Our method performs similarly in classification accuracy compared to common multi-task joint training, but performs better in training efficiency.

关键词： Training Learning systems Deep learning Constellation diagram Memory management Modulation Aerospace electronics

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Remote Healthcare Monitoring System for Aging Population Based on IoT and Big Data Analysis

Remote Healthcare Monitoring System for Aging Population Bas...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Qiang Liu Songlin Sun Liang Gong Zhongle Wu Mohamed Cheriet Michel Kadoch National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Academy of Broadcasting Planning NRTA China ETS University of Quebec Montreal Canada

ISBN: (数字)9781728157849

ISBN: (纸本)9781728157856

As the population ages, this will put tremendous pressure on pensions and the medical system. Health insurance for the elderly poses a major challenge to the allocation of limited medical resources. The Internet of Things (IoT) is widely regarded as a potential solution to relieve pressure on healthcare systems. With the rapid development of wireless communication technologies such as backscatter communication, Wi-Fi and 5G, in vivo and in vitro medical devices have been greatly developed. In this article, we propose a real-time big data healthcare analysis system based on the combination of shallow neural networks and deep neural networks. The system consists of two parts: the detection of fog layer abnormality based on shallow neural network and the diagnosis of cloud disease based on deep neural network. The system can not only make full use of the real-time nature of the fog layer but also give full play to its powerful computing power based on reducing the computing pressure of the cloud layer.

关键词： Cloud computing Neural networks Sociology Real-time systems Internet of Things Statistics Medical diagnostic imaging

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R-TAR: A Resilient Traffic Anomaly Recognition Mechanism for Backbone network 10

R-TAR: A Resilient Traffic Anomaly Recognition Mechanism for...

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10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018

作者： Li, Xueyu Zhang, Xu Wang, Dongbin School of Network Education Beijing University of Posts and Telecommunications Beijing100876 China National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Ministry of Education and Trustworthy Distributed Computing and Service School of CyberSpace Security Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781538658369

The development of backbone networks leads to an increase in heterogeneous anomalies and cyberspace situation complexity, and traditional traffic monitoring cannot meet multiple-point concurrency design and resilience demand. Situational awareness from the military field can effectively deal with the anomalies with significant temporal-spatial characteristics. Considering the requirement of automatic control in practical application, the concept of resilient Cyber Physical System (R-CPS) is presented. Based on this concept, we propose resilient traffic anomaly recognition (R-TAR) which is a refined mechanism for spatiotemporal traffic anomaly assessment in backbone and develop the TAR algorithm to multi-HCRF (Hidden Conditional Random Fields). R-TAR provides a CPS-based feedback control loop integrating traffic sensing, decision making and information feedback. In view of the characteristics of backbone network traffic, we develop multi-HCRF to extract temporal and spatial situation from packet-granularity to flow-granularity for better performance, and obtain the overall-network-wide situation for feedback. The experimental evaluation establishes a proof-of-concept for the proposed model based on R-TAR, confirming the practicality and performance improvement of our approach on real datasets. © 2018 IEEE.

关键词： Cyber Physical System

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A Particle Swarm Optimization based Feature Selection Method for Accident Severity Analysis

A Particle Swarm Optimization based Feature Selection Method...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Chenye Qiu Xingquan Zuo Fei Xiang School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

Reducing accident severity is an effective mean to improve road safety level. Many researches have been done to identify the risky features which would influence the accident severity. Many risky features need to be considered when building accident severity analysis model, including driver, highway, vehicle, accident, and atmospheric factors. Some of those features are irrelevant of redundant. Using those features would decrease the performance of the prediction model and bring additional computational burden. However, there are very few researches on feature selection in accident severity analysis problem to date. In this paper, we propose a particle swarm optimization (PSO) based feature selection method for accident severity analysis. The proposed method can obtain a reduced number of feature subset from the original feature pool. In order to testify the method, the accident data of Beijing from 2008 to 2010 are used for experiment. Experimental results show the proposed PSO based feature selection method can significantly reduce the number of features while improving the classification accuracy. Moreover, it can provide better interpretation of the accident severity analysis model.

关键词： Conferences Automation Control systems Intelligent systems

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Blockchain-based Intelligent Hospital Security and Data Privacy Construction

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Journal of Physics: Conference Series 2019年第5期1187卷

作者： Qiuzi Huang Shuyu Chen Hui Zhao Junhao Wen Network Distributed Computing Laboratory Big Data and Software Institute Huxi Campus of Chongqing University Chongqing China Huxi Campus of Chongqing University no. 55 Chengnan Road Shapingba District Chongqing China Chongqing University 174 Shazheng Street Shapingba District Chongqing China Service Computing Laboratory Big Data and Software Institute Huxi Campus of Chongqing University Chongqing China

With the rapid development of medical information services, the construction of intelligent hospitals is opening up a new mode of medical treatment in the health care industry. Medical data is gradually becoming more and more important, while it also faces some challenges, among which the most urgent problem to be solved is data security and privacy protection. In the construction of intelligent hospitals, the safety issues among the basic information of patients, the protection of medical information and inter-institutional information sharing have become the focus at this stage. Blockchain technology, with highly security, reliable architecture and algorithm design have operated stably in the financial industry for more than seven years. The related innovative technologies such as distributed ledgers, smart contracts, symmetric encryptions and consensus mechanisms are used widely in many fields. This paper will take the demonstration construction of Chongqing Intelligent Hospital as an example, which tries to combine the blockchain, homomorphic encryption and zero-knowledge proof technology to carry out the research on the security construction and data privacy protection of intelligent hospitals.

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Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in HBase

Implementing an Inference Engine for RDFS/OWL Constructs and...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Zhengbo Liu Wenbin Yao Dongbin Wang National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Ministry of Education for Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

The Inference Engine is an integral part of Semantic Data Stores. In this paper, we describe our experience of implementing a scalable inference engine for HBase Semantic Data Store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include i) inferencing based on semantics of RDFS/OWL constructs and user-defined rules, and ii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of the HBase Database. The paper describes the inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using LUBM semantic datasets demonstrates the effectiveness of our approach.

关键词： Engines Ontologies Semantics Cognition Resource description framework OWL Databases

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Fog computing for vehicular Ad-hoc networks: paradigms, scenarios, and issues

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The Journal of China Universities of Posts and Telecommunications 2016年第2期23卷 56-65,96页

作者： Kang Kai Wang Cong Luo Tao School of Software Engineering Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts Telecommunications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts Telecommunications

Vehicular Ad-hoc networks（VANETs） are kinds of mobile Ad-hoc networks（MANETs）, which consist of mobile vehicles with on-board units（OBUs） and roadside units（RSUs）. With the rapid development of computation and communication technologies, peripheral or incremental changes in VANETs evolve into a revolution in process. Cloud computing as a solution has been deployed to satisfy vehicles in VANETs which are expected to require resources（such as computing, storage and networking）. Recently, with special requirements of mobility, location awareness, and low latency, there has been growing interest in research into the role of fog computing in VANETs. The merging of fog computing with VANETs opens an area of possibilities for applications and services on the edge of the cloud computing. Fog computing deploys highly virtualized computing and communication facilities at the proximity of mobile vehicles in VANET. Mobile vehicles in VANET can also demand services of low-latency and short-distance local connections via fog computing. This paper presents the current state of the research and future perspectives of fog computing in VANETs. Moreover, we discuss the characteristics of fog computing and services based on fog computing platform provided for VANETs. In this paper, some opportunities for challenges and issues are mentioned, related techniques that need to be considered have been discussed in the context of fog computing in VANETs. Finally, we discuss about research directions of potential future work for fog computing in VANETs. Within this article, readers can have a more thorough understanding of fog computing for VANETs and the trends in this domain.

关键词： VANET fog computing cloud computing application vehicular cloud computing

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Secure transmission with artificial noise in heterogeneous massive MIMO network 1st

Secure transmission with artificial noise in heterogeneous m...

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1st International Congress on Signal and Information Processing, networking and Computers, ICSINC 2015

作者： Li, Chang Sun, Songlin Liu, Wei Huang, Hai School of Information and Communication Engineering Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Network and Platform M2M & Internet of Things Institute China Mobile Research Institute Beijing China

ISBN: (纸本)9781138028814

Heterogeneous network (HetNet) is a promising technology to improve the capacity of future generations of cellular network, in which a mobile station can be served by multiple Base Stations (BSs) with different scales of coverage range, including short range Low Power Nodes (LPNs). In this paper, we study security provisioning in heterogeneous massive Multiple-Input–Multiple-Output (MIMO) systems. Specifically, we consider secure downlink transmission in a massive MIMO system with Matched-Filter (MF) precoding and Artificial Noise (AN) generation at the BS, and transmit the signal to the MT through the LPN in the presence of a passive multi-antenna eavesdropper. Thereby, we consider an AN shaping matrix, where the AN is transmitted in the null-space of the matrix formed by all MT channels. Our analytical and numerical results reveal that, in massive MIMO systems employing MF precoding and AN generation, the Signal-to- Interference-and Noise Ratio (SINR) of intended MTs in LPNs is higher than eavesdropper, we can also obtain the positive secrecy capacity which prove this method can guarantee the secure transmission. © 2016 Taylor & Francis Group, London.

关键词： Heterogeneous networks

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