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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Qi, Guangfei Qu, Zhihao Lyu, Shen-Huan Jia, Ninghui Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819601189

Personalized Federated Learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature information and ignores global information, the generalization ability of the feature extractor is limited. To improve the performance of the local model, a feasible approach is to make the local feature extractor more generalized. However, prior work requires the transmission of additional feature data beyond the transmission of model parameters, which leads to privacy leakage and higher communication overhead. To address these shortcomings, we propose a PFL algorithm with feature alignment via knowledge distillation, named PFAKD. PFAKD enhances the training of local feature extractors by explicitly aligning each sample’s local features with global features, providing more detailed guidance. Meanwhile, it avoids additional communication overhead and the risk of privacy leakage. We conduct extensive experiments in heterogeneous data scenarios. PFAKD outperforms other state-of-the-art methods by up to 4.35% in terms of model accuracy. Our code is available at https://***/fei0829/PFAKD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Rainfall Recognition Based on Multi-Feature Fusion of Audio Signals 4

Rainfall Recognition Based on Multi-Feature Fusion of Audio ...

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4th International Conference on computer science and Management technology, ICCSMT 2023

作者： Li, Xueying He, Yong Peng, Anlang Yao, Kaixue State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang Guizhou550025 China Guizhou Zhaoxin Digital Technology Co. Ltd Guiyang Guizhou China

ISBN: (纸本)9798400709517

Rainfall recognition is an important meteorological forecasting task with significant implications for sectors such as agriculture, urban management, and transportation. Traditional rainfall measurement typically employs mechanical rain gauges, which impose strict environmental requirements, often necessitate manual intervention, and offer limited spatiotemporal resolution. To address this challenge, this study employs rain sound for rainfall detection and utilizes feature fusion techniques, combining multiple features of varying scales to capture multiscale information within rainfall data. Furthermore, it introduces dilated convolutional networks to enlarge the receptive field and enhance the model's understanding of global information. In this study, STM32F7 is employed as the processor for rain sound acquisition, and rainfall events are classified using tipping bucket rain gauges, resulting in the creation of a rainfall audio dataset. This dataset is partitioned into training and testing sets, and a deep learning-based classification model is constructed. Experimental results demonstrate that feature fusion and dilated convolutional networks significantly improve the accuracy and performance of rainfall classification. © 2023 ACM.

关键词： Rain

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Enhance Smart Grid Security With A Coordinated Cyber-Physical Defensive Mechanism 41

Enhance Smart Grid Security With A Coordinated Cyber-Physica...

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第41届中国控制会议

作者： Zhenyong Zhang Xin Che Xuguo Jiao Jingpei Wang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou University College of Control Science and Engineering Zhejiang University School of Information and Control Engineering Qingdao University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Smart grid,as one of the largest evolutionary critical infrastructures,witnesses the deep integration of cyber technologies and physical ***,recent events show that the vulnerabilities exposed in the cyber side and the physical side can be exploited to cause coupled and severe ***,the adversaries are able to mask the pernicious physical attack using the cyber attack by compromising the digitized data transmitted to the control *** cooperative cyber-physical attack(CCPA) is stealthy to the current defense *** this article,we propose a cyber-physical defensive mechanism(CPDM) to thwart the *** changing the physical value of the line admittance,the model parameters known by the adversary are not useful anymore,which is the basic idea to design the *** implementation of CPDM depends on the deployment of the distributed flexible AC transmission system(D-FACTS).To reduce the infrastructure cost for the CPDM,we investigate the minimum number of required D-FACTS devices to thwart the *** consider both weak constraints on the CCPA and CPDM and practical limitations for the CCPA and *** each case,we propose a method to compute the optimal deployment of D-FACTS devices,that is,where the DFACTS device should be deployed.

关键词： CPA Enhance Smart Grid Security With A Coordinated Cyber-Physical Defensive Mechanism Security

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LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association

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computers, Materials & Continua 2024年第10期81卷 1449-1470页

作者： Suya Li Ying Cao Hengyi Ren Dongsheng Zhu Xin Xie Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng475001China College of Information Science and Technology&Artificial Intelligence Nanjing Forestry UniversityNanjing210037China School of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China

Multi-object tracking(MOT)has seen rapid improvements in recent ***,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking *** from some existing methods,which discarded the low-quality targets or ignored low-quality target ***,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality *** the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data ***,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target ***,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking *** experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).

关键词： Low-quality targets association strategy feature fusion multi-object tracking tracking-by-detection

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An Enhanced Puma Optimizer for Cloud Computing Task Scheduling

An Enhanced Puma Optimizer for Cloud Computing Task Scheduli...

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Robotics, Artificial Intelligence and Intelligent Control (RAIIC), International Conference on

作者： Li Bang Wu Shaobo Li Fengbin Wu Rongxiang Xie College of Computer Science and Technology Laboratory of Public Big Data Guizhou University State Key Guizhou China State Key Laboratory of Public Big Data Guizhou University Guizhou China College of Computer Science and Technology Guizhou University Guizhou China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

Aiming at the lack of accuracy of puma optimizer in cloud task scheduling, an improved task scheduling method based on puma optimizer is proposed by combining the intermediate point sample learning strategy and S-shaped transfer function (MSPO). Among them, the middle point sample learning strategy improves the searching ability of the algorithm, while the S-shaped transfer function reduces the response time of the system, thus speeding up the execution speed of the algorithm. By testing different scale problems, it is proved that MSPO can effectively reduce the cost of consumption.

关键词： Cloud computing Costs Processor scheduling Metaheuristics Transfer functions Scheduling Time factors

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Improving Adaptive Runoff Forecasts in data-Scarce Watersheds Through Personalized Federated Learning 27th

Improving Adaptive Runoff Forecasts in Data-Scarce Watershe...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xie, Zaipeng Zhang, Xiangqin Wang, Yunfei Jie, Xuanyao Fang, Wenhao Cai, Yanping Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031781827

Runoff forecasting plays a crucial role in water resource management and flood mitigation, but it often faces significant challenges due to data deficiency and decentralized datasets. Inadequate hydrological data in many watersheds hinders the development of accurate prediction models, while organizational barriers and concerns about data privacy lead to information silos, preventing effective collaborative modeling. To address these issues, we propose FedHydroDSW, a novel personalized federated learning framework for runoff forecasting in data-scarce watersheds. Our approach enables decentralized model training across multiple organizations without requiring direct data sharing, thus preserving data privacy. FedHydroDSW incorporates unique model similarity metrics and parameter adaptation methods, utilizing insights from data-rich areas to refine forecasts in data-scarce regions. Experiments on the CAMELS dataset show that FedHydroDSW significantly outperforms standalone and conventional federated learning methods, with an average 30.1% increase in NSE, and reductions of 28% in RAE and 26.6% in RMSE across multiple data-scarce basins. By enabling secure cross-silo learning, our FedHydroDSW strategy paves the way toward more equitable runoff forecasting globally. The customized training and pattern recognition techniques represent major progress in deploying federated intelligence for impactful hydrological predictions. Our code is publicly available at https://***/xyjie37/FedHydroDSW. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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A Verifiable Privacy-Preserving Outsourced Prediction Scheme Based on Blockchain in Smart Healthcare

A Verifiable Privacy-Preserving Outsourced Prediction Scheme...

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2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023

作者： Li, Ta Tian, Youliang Xiong, Jinbo Wang, Linjie College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou350117 China School of Data Science Tongren University Tongren554300 China

ISBN: (纸本)9798350302301

The swift progression of the Internet of Things and the extensive integration of machine learning have spurred the growth of intelligent healthcare. Many intelligent healthcare devices, limited by their own computing and storage resources, require outsourcing data analysis tasks to cloud platforms for efficient and accurate results. Unfortunately, malicious cloud services lead to privacy breaches in outsourced data and untrustworthiness in learning models. To address these challenges, this paper proposes a verifiable privacy-preserving outsourced prediction scheme based on blockchain in smart healthcare (VPOL). Specifically, by incorporating blockchain technology into VPOL, we build a robust and scalable framework to prevent falsification of outsourced data and learning models in a decentralized and transparent manner. Then, we design a training committee approach to ensure the reliability of outsourced prediction and employ homomorphic encryption and commitment scheme to protect the privacy and integrity of the data. Finally, theoretical analysis proves the effectiveness and security of VPOL. Sufficient experiments demonstrate that VPOL achieves the approximate accuracy of the plaintext. © 2023 IEEE.

关键词： Blockchain

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Hierarchical Scalable data Lake Design for Game Engine 8

Hierarchical Scalable Data Lake Design for Game Engine

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8th International Conference on big data and Information Analytics, bigDIA 2022

作者： Yang, Mingbao Zhou, Peng Li, Shaobo Pu, Ruiqiang College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China School of Mechanical Engineering Guizhou University Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

With the rapid development of information technology, people are more and more inseparable from smart devices, and people and devices produce a large amount of data all the time. How to store and analyze massive data has become a research hotspot of the times. However, researchers mainly study the high reliability, high availability, high fault tolerance, high throughput, and high concurrent processing of big data systems, but there are few studies on designing hierarchical and scalable data lakes. In this study, a layered and extensible design of the data lake is carried out, which reduces the complexity of implementing business indicators during data analysis, and at the same time improves code reuse, scalability, development efficiency and code security. In addition, in order to avoid the problem of data accumulation or even job execution failure due to the skew of ip dimension table data, this paper designs the ip dimension table rowkey. This paper designs a set of real-time and offline big data analysis data lake architecture based on game engine and applies it to the actual production environment. © 2022 IEEE.

关键词： big data

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Lightweight IoT-Oriented Verifiable Computing Scheme in Cloud Computing Circumstance

Lightweight IoT-Oriented Verifiable Computing Scheme in Clou...

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Cyber science and technology Congress (CyberSciTech), IEEE

作者： Xintao Pei Yuling Chen Yun Luo Zaidong Li Jianqi Wei State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

The advent of the Internet of Things (IoT) has precipitated the creation of a plethora of intelligent applications that rely on data gathered from a multitude of “things.” In the context of big data processing, cloud computing plays a pivotal role, offering a range of services for data computation and analysis. However, there is a risk that cloud service providers may compromise data privacy or deliver inaccurate processing results, which could give rise to concerns about data reliability. Concurrently, the majority of cloud users lack the requisite resources to process large-scale data or verify the accuracy of the information they have processed. This presents a significant challenge for cloud verification, particularly in the context of encrypted user data and cloud-based processing. Despite this, current research has not fully addressed this issue. In this paper, we introduce a lightweight verifiable computing scheme that ensures trusted cloud computing. Our scheme allows for outsourced computations on encrypted data and supports public verification of the results. Performance analysis and algorithm implementation demonstrate the efficiency and effectiveness of the scheme. We also provide a detailed analysis and comparison of the strengths and weaknesses of each protocol.

关键词： Cloud computing Protocols data processing Performance analysis Outsourcing Encryption Cryptography Internet of Things Reliability Protection

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Range Proof-Based Noise Filtering Mechanism for loT Differential Privacy

Range Proof-Based Noise Filtering Mechanism for loT Differen...

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Cyber science and technology Congress (CyberSciTech), IEEE

作者： Jianqi Wei Yuling Chen Yun Luo Zaidong Li State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

With the rapid development of Internet of Things (loT) technology, the vast amount of data generated by its devices has raised widespread concern for user privacy pro-tection. Differential Privacy, as a stringent privacy protection measure, plays a significant role in safeguarding individual data. However, implementing Differential Privacy in the loT environment faces challenges in ensuring data utility. This paper focuses on proposing a Verifiable Differential Privacy (VDP) scheme based on zero-knowledge proofs, under the premise of ensuring data utility. The scheme requires data publishing entities to provide publicly verifiable proofs to confirm the reliability of the dataset and the effectiveness of privacy protection. By introducing a commitment mechanism and range proofs, our model not only protects the interests of data users but also enhances trust in the enforcement of privacy protection measures. The experimental results show that the scheme can effectively filter out unreasonable Differential Privacy noise, ensuring the privacy and reliability of data, offering a new approach to data privacy protection in the loT field.

关键词： Differential privacy Publishing Noise Information filters data models Vectors Reliability Internet of Things Protection Faces

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