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Heterogeneous Device Collaboration Based Federated Learning for Big Data Applications

IEEE Transactions on Big Data 2024年 1-11页

作者： Wang, Wenhua Yang, Quan Liang, Yuzhu Xu, Yang Liu, Qin Wang, Tian School of Artificial Intelligence Beijing Normal University Beijing China College of Computer Science and Technology Huaqiao University Xiamen China College of Computer Science and Electronic Engineering Hunan University Changsha China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China

In the era of Big Data, artificial intelligence and information science are the key technologies to extract the value of data and enhance the competitiveness of enterprises. The characteristics of distributed, small-scale, and sparse lead to the isolated data island problem. To solve these problems, Federated Learning is proposed. However, a large number of terminal models need to be uploaded to the server in Federated Learning, especially for the actual scenario of Internet of Things. Therefore, huge communication costs are required which dramatically increases the pressure on the backbone network. Furthermore, the low quality of the local model will lead to decreased accuracy and convergence rates of the model. To overcome the above limitations, we propose heterogeneous device collaboration based federated learning (HDCFL), which constructs a three-layer structure for Federated Learning by leveraging edge computing and designs a heterogeneous device collaboration method that groups the terminals based on their computing power, communication time, and data volume to train the model. Then, we conduct a theoretical analysis of the proposed algorithm which verifies its advantage. At last, the experimental result demonstrates that the proposed algorithm consistently achieves superior performance in terms of both convergence speed and accuracy compared with state-of-the-art baselines. IEEE

关键词： Competition

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ACNet: Enhancing Occlusion Awareness and Multi-Scale feature Capture for Cervical Cell Segmentation

ACNet: Enhancing Occlusion Awareness and Multi-Scale feature...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Hailong Zhang, Yangyang Wang, Zhengxia Hainan University School of Computer Science and Technology Haikou China Hainan University School of Information and Communication Engineering Haikou China

ISBN: (纸本)9798350386226

Cell segmentation is critical for early cervical cancer screening, yet it faces challenges inherent in cervical cell images, such as cell occlusion and scale diversity. In this paper we proposed an innovative cell image segmentation framework called ACNet. This framework employs an Easy-to-Difficult Indirect Decoupling Strategy (EDS), combined with a Feature Refinement Module (FRM), to improve the model's perception of occluded instances and invisible regions. Additionally, we designed a Squeeze-and-Excitation Module (SEM) and a Point Supervision Module (PSM) to improve the model's ability to capture features of larger occluded cells and smaller cells in low-contrast images, respectively. We verified our method using two occlusion cytology image segmentation datasets, and compared it with state of the art segmentation methods. ACNet achieved superior segmentation results on cervical cell images with cell occlusion and scale diversity. This study provides an important driving force for early screening for cervical cancer. © 2024 IEEE.

关键词： Image segmentation

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Leveraging U-Net Variants for High-Precision Flood Mapping: A Performance Evaluation on the ETCI Dataset 2

Leveraging U-Net Variants for High-Precision Flood Mapping: ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thirugnanasammandamoorthi, Puviyarasi Ghosh, Debabrata Dewangan, Ram Kishan Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331534950

Flood mapping is a crucial task in mitigating the adverse impacts of flooding by providing accurate spatial information about inundated areas. This study explores flood mapping using the ETCI dataset, leveraging the U-Net model and its enhanced variants. Initially, a basic U-Net architecture is employed to generate flood maps, followed by progressively improved versions incorporating batch normalization, dropout, and attention mechanisms. These modifications aim to enhance model generalization, robustness, and feature extraction capabilities. For the purpose of evaluating the model's performance, metrics like accuracy, F1-score, and Intersection over Union (IoU) are utilised. The generated flood maps demonstrate the potential of advanced U-Net architectures for precise and efficient flood delineation, providing a valuable tool for disaster management and planning. © 2024 IEEE.

关键词： Disaster prevention

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of computer science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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KSKV:Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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computer Modeling in engineering & sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development Research Center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of Information Science and Technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph ***,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data *** studies would simultaneously collect the frequencies of keys and the mean of values associated with each ***,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility *** the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under ***,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration *** strategies are adapted for scenarios in which users possess either a single or multiple key-value ***,we demonstrate that the variance of KS-UE is lower than that of existing *** claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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Joint Uplink and Downlink Robust Transmission for Cell-Free Networks

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IEEE Transactions on Wireless communications 2024年第10期23卷 15308-15321页

作者： Zhang, Guangyang Lu, Yang Li, Mingchun Zhong, Zhangdui Quek, Tony Q. S. Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronic and Information Engineering Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing100044 China Singapore University of Technology and Design Tampines487372 Singapore Yonsei University Seoul03722 Korea Republic of

This paper investigates the joint uplink (UL) and downlink (DL) robust transmission design for cell-free networks. The total data amount of the UL and DL transmissions is maximized in the presence of the statistical channel state information error by optimizing the UL digital combiners, UL transmission power, DL transmit beamforming vectors and UL/DL time allocation subject to the tolerable UL/DL outage probability constraints, the UL/DL minimum data amount requirements and the UL/DL power budgets. To tackle the variables coupling in outage probability constraints, the property of perspective function and the quadratic transform are applied. Then, the Bernstein-type inequality is used to derive the computationally tractable forms of the outage probability constraints. The considered problem is further decomposed into four subproblems and solved by the proposed alternating optimization (AO)-based algorithm. Numerical results show the proposed algorithm outperforms three existing AO-based baselines in terms of convergence speed and optimality performance. The impacts of the UL transmission power and tolerable outage probability on the UL/DL transmission and time allocation are revealed. Moreover, the effective APs for each user are defined and illustrated to show the coordination among the APs. © 2024 IEEE.

关键词： Channel state information

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PIM-ST: a New Paraphrase Identification Model Incorporating Sequence and Topic Information 4

PIM-ST: a New Paraphrase Identification Model Incorporating ...

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4th International Symposium on computer technology and Information science, ISCTIS 2024

作者： Mao, Kaili Zhao, Qingjuan Beijing Institute of Control Engineering Beijing China University Beijing Computer School Beijing Information Science and Technology Beijing China

ISBN: (纸本)9798350354560

Paraphrase identification, with the objective to determine whether two sentences are paraphrases of each other, has fostered many applications including natural language inference and document retrieval. Traditional machine learning models for paraphrase identification are generally based on hand-crafted features. Recent deep neural network models generally take semantic vectors as inputs and generate classification results directly. However, existing solutions still have much room for improvement. First, existing models do not fully exploit sequence information (i.e., the order of words) and topic information, which are important for text under-standing. Second, although deep models achieve promising results, they require a large amount of labelled data and have low interpretability. To cope with these problems, we propose a new model called PIM-ST (Paraphrase Identification Model Incorporating Sequence and Topic Information). Specifically, besides semantic information, PIM-ST extracts sequence information and topic information, and adopts a simple machine learning model LightGBM as the classifier. We conduct comparative experiments and ablation studies on two public datasets Microsoft Research Paraphrase Corpus (MSRP) and Quora Question Pairs. Experimental results show that PIM-ST achieves better results than existing state-of-the-art models. © 2024 IEEE.

关键词： Semantics

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Privacy-Preserving Arbitrary Hollow Polygon Geometric Range Query Scheme in Fog Computing-Based IoVs 9

Privacy-Preserving Arbitrary Hollow Polygon Geometric Range ...

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Chen, Dongdong Jiang, Luyao Wang, Yi Gu, Ke Changsha University of Science & Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798350383706

Geometric range query is an essential operation in many applications, including location-based service, querying on sensor networks, and computational geometry. In the Internet of Vehicles (IoVs) environment, the range query operations allow for retrieving vehicles or events within a specific spatial range, enabling applications such as traffic monitoring, incident detection, and task outsourcing. However, with the issues of latency, location and query range privacy, and geometric query range limits, the application of range query in IoVs still faces a number of serious challenges to its prosperity. In this paper, we design arbitrary hollow polygon preprocessing and points in polygon judgment strategies that support arbitrary hollow polygon range query. Furthermore, by integrating fog computing into IoVs, the low latency of the overall process is met. Finally, the theoretical analysis and experimental results show that the proposed scheme is secure and efficient. © 2023 IEEE.

关键词： Fog computing

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Reinforcement Learning Based Edge Microservice Migration Strategy under Resource Constraints 5

Reinforcement Learning Based Edge Microservice Migration Str...

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5th International science and technology Conference Modern Network Technologies, MoNeTec 2024

作者： Dong, Mengxue Huo, Dongkun Zhang, Hong Zhang, Yan Hao, Yixue Hu, Long Chen, Min Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China South China University of Technology School of Computer Science & Engineering Guangzhou China National University of Defense Technology College of Information and Communication Changsha China

ISBN: (纸本)9798350369007

Service migration can solve the issue of frequent user switching caused by the restricted coverage of edge cloud, which is currently a major focus of research. Simultaneously, microservice architecture gains traction in businesses for its benefits in maintainability and scalability. Existing migration strategies mainly target monolithic services, overlooking the complexities of microservice migration scenarios. Considering the limited edge cloud resources and the flexible combination of microservices to be migrated individually in the microservice migration process, we construct a microservice migration system architecture to solve this problem with the goal of minimizing latency and energy consumption. We transform the microservice migration problem to a Markov decision process and propose an intelligent microservice migration algorithm under resource constraints based on reinforcement learning algorithm. Simulation results indicate that our algorithm reduces user request latency and system energy consumption, while enhancing QoS compared to current approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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Traffic Sign Recognition Based on Improved YOLOv5s 9

Traffic Sign Recognition Based on Improved YOLOv5s

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Zhang, Jin Cao, Fan Zhang, Jianming Li, Ying Wu, Sheng Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798350383706

In the field of autonomous driving, it is of great significance for autonomous driving to detect traffic signs accurately. This paper proposes a method named MR_YOLO. This method addresses the issue of inadequate accuracy that plagues current systems. More specifically, to address varying sizes of traffic signs in images, our strategy refined anchor box dimensions using K-Means++ clustering and fine-tuned the model to enhance traffic sign recognition efficacy. Next, a multi-scale feature extraction model, C3Res2Net, is formed by combining the C3 and Res2Net models to address the different scales and sizes of traffic signs. This approach enables the network to accurately capture comprehensive information and intricate details of traffic signs, thus enhancing recognition capabilities. Experimental data reveal competitive outcomes when tested on the CCTSDB dataset. The method achieves values of 92.8% and 88.4% in precision and mAP, respectively. © 2023 IEEE.

关键词： Object detection

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