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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial Intelligence and Key Laboratory of mathematics Informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of software Development Environment and Advanced Innovation center for Future Blockchain and Privacy computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for mathematical Sciences and engineering Research center of Financial computing and Digital engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

Chaotic Map-Based Authentication and Key Agreement Protocol with Low-Latency for Metasystem

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Computers, Materials & Continua 2024年第3期78卷 4471-4488页

作者： Guojun Wang Qi Liu School of Electronics&Information engineering Nanjing University of Information Science&TechnologyNanjing210044China Yancheng Polytechnic College Yancheng224000China Jiangsu Province engineering Research center of Advanced computing and Intelligent Services School of SoftwareNanjing University of Information Science and TechnologyNanjingChina

With the rapid advancement in exploring perceptual interactions and digital twins,metaverse technology has emerged to transcend the constraints of space-time and reality,facilitating remote AI-based *** this dynamic metasystem environment,frequent information exchanges necessitate robust security measures,with Authentication and Key Agreement(AKA)serving as the primary line of defense to ensure communication ***,traditional AKA protocols fall short in meeting the low-latency requirements essential for synchronous interactions within the *** address this challenge and enable nearly latency-free interactions,a novel low-latency AKA protocol based on chaotic maps is *** protocol not only ensures mutual authentication of entities within the metasystem but also generates secure session *** security of these session keys is rigorously validated through formal proofs,formal verification,and informal *** confronted with the Dolev-Yao(DY)threat model,the session keys are formally demonstrated to be secure under the Real-or-Random(ROR)*** proposed protocol is further validated through simulations conducted using VMware workstation compiled in HLPSL language and C *** simulation results affirm the protocol’s effectiveness in resisting well-known attacks while achieving the desired low latency for optimal metaverse interactions.

关键词： Metasystem authentication and key agreement chaotic map secure communication

Reinforcement learning based edge computing in B5G

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Digital Communications and Networks 2024年第1期10卷 1-6页

作者： Jiachen Yang Yiwen Sun Yutian Lei Zhuo Zhang Yang Li Yongjun Bao Zhihan Lv School of Electrical and Information engineering Tianjin UniversityTianjin300072China Technology and Data center ***Beijing100176China School of Data Science and software engineering Qingdao UniversityQingdao266101China

The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology *** the same time,Beyond 5G will become one of the important supports for the development of edge computing *** paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge *** trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and *** results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.

关键词： Reinforcement learning Edge computing Beyond 5G Vehicle-to-pedestrian

Blockchain Meets Generative Behavior Steganography:A Novel Covert Communication Framework for Secure IoT Edge computing

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Chinese Journal of Electronics 2024年第4期33卷 886-898页

作者： Yuanlong CAO Junjie LI Kailin CHAO Jianmao XIAO Gang LEI School of Computer Information engineering Jiangxi Normal University Jiangxi Blockchain Data Security and Governance engineering Research center Jiangxi Normal University School of software Jiangxi Normal University

The rapid development of Internet of things(Io T) and edge computing technologies has brought forth numerous possibilities for the intelligent and digital future. The frequent communication and interaction between devices inevitably generate a large amount of sensitive information. Deploying a blockchain network to store sensitive data is crucial for ensuring privacy and security. The openness and synchronicity of blockchain networks give rise to challenges such as transaction privacy and storage capacity issues, significantly impeding their development in the context of edge computing and Io T. This paper proposes a reliable fog computing service solution based on a blockchain fog architecture. This paper stores data files in the inter planetary file system(IPFS) and encrypts the file hash values used for retrieving data files with stream cipher encryption. It employs a steganographic transmission technique leveraging Alpha Zero's Gomoku algorithm to discretely transmit the stream cipher key across the blockchain network without a carrier, thus achieving dual encryption. This approach aims to mitigate the storage burden on the blockchain network while ensuring the security of transaction data. Experimental results demonstrate that the model enhances the transmission capacity of confidential information from kilobytes(KB) to megabytes(MB) and exhibits high levels of covert and security features.

关键词： Steganography Ciphers Privacy Data privacy Blockchains Encryption Internet of Things

Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of Computer Science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of software Development Environment Beijing 100191China School of Computer Science and engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of mathematical engineering and Advanced computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology engineering Research center of Digital Forensics Ministry of Education Department of Computer and software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of mathematical engineering and Advanced computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Image coding Image color analysis Quaternions Forensics Transform coding Color Feature extraction

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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Computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and engineering National Institute of Technology JamshedpurJamshedpurIndia School of computing Science and engineering VIT University BhopalBhopal466114India center for Industrial software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

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Computer Modeling in engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic Department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of Physics and Information engineering Jiangsu Second Normal UniversityNanjing211200China School of computing and mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State Key Laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province engineering Research center of Basic Education Big Data Application Jiangsu Second Normal UniversityNanjing211200China School of software engineering Quanzhou Normal UniversityQuanzhou362000China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques ***:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid ***,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask ***,we propose the DualLoss function,tailored to the thyroid nodule localization and classification *** balances the learning of the localization and classification tasks to help improve the model’s generalization ***,we introduce strategies for augmenting the ***,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid ***:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and *** results show that ThyroidNet outperformed these methods in localizing and classifying thyroid *** achieved improved accuracy of 3.9%and 1.5%,***:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis *** research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis

Towards High-Performance Graph Processing: From a Hardware/software Co-Design Perspective

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Journal of Computer Science & Technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National engineering Research center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem