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MSDGSD: A Scalable Graph Descriptor for Processing Large Graphs

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第3期11卷 3594-3605页

作者： Ali, Muhammad Said, Anwar Safder, Iqra Ul Hassan, Saeed Aljohani, Naif Radi Shabbir, Mudassir Informat Technol Univ Lahore 54000 Pakistan Vanderbilt Univ Nashville TN 37235 USA Fast NUCES Lahore 54000 Pakistan King Abdulaziz Univ Jeddah 21589 Saudi Arabia Informat Technol Univ Lahore 54000 Pakistan

Graph representation methods have recently become the de facto standard for downstream machine learning tasks on graph-structured data and have found numerous applications, e.g., drug discovery & development, recommendation, and forecasting. However, the existing methods are specially designed to work in a centralized environment, which limits their applicability to small or medium-sized graphs. In this work, we present a graph embedding method that extracts graph representations in a distributed environment with independent and parallel machines. The proposed method is built-upon the existing approach, distributed graph statistical distance (DGSD), to enhance the scalability on large graphs. The key innovation of our work lies in the proposition of a batching mechanism for client-server message passing, which reduces communication overhead during the computation of the distance matrix. In addition, we present a sampling approach for computing pairwise distances between the nodes to compute the desired graph embedding. Moreover, we systematically explore six distinct variations of a distributed graph embeddings and subsequently subject them to comprehensive evaluation. Our extensive evaluations on over 20 graph datasets and ten baseline methods demonstrate improved running time and comparative classification accuracy compared to state-of-the-art embedding techniques.

关键词： distributed computing graph classification graph embedding parallel processing

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Dynamic Secure Multi Broad Network for Privacy Preserving of Streaming Data

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第4期8卷 3152-3165页

作者： Cao, Xiao-Kai Chen, Man-Sheng Wang, Chang-Dong Lai, Jian-Huang Huang, Qiong Chen, C. L. Philip Sun Yat sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510275 Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510275 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510275 Peoples R China

distributed computing as a widely concerned research direction needs to use the data training model of users, making the security of users' private data become a challenging problem to be solved. At present, federated learning is the mainstream research method to solve this problem. However, federated learning is not good at distributed training on streaming data. In real scenarios, the client's data is usually continuously updated streaming data. In this paper, we propose Dynamic Secure Multi Broad Network (DSMBN), which is a novel privacy computing framework completely different from federated learning. In DSMBN, we design three interactive communication protocols to handle streaming data in different scenarios. The function of the protocol is to use random mapping to encrypt data during the interaction. The protocol ensures that the client's original data does not leave the local server when generating mapped features. The central server uses the resulting mapped features (essentially encrypted data) instead of the original data to train machine learning models. In theoretical analysis, we analyze the first protocol's security, communication costs, and computational complexity. In the experiment, we design seven experimental scenarios, including quantity balance, Non-IID data distribution and streaming data, and compare them with several mainstream privacy protection machine learning methods. The experimental results show that compared with centralized training without privacy protection, DSMBN can achieve the same test accuracy under the premise of protecting private data security. Compared with mainstream federated learning methods, DSMBN can achieve higher accuracy in the Non-IID scenarios and save computing time and communication resources.

关键词： Streaming data secure multi-party computing distributed computing broad learning system

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Research on Transparent Access Technology of Government Big Data

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2024年第3期31卷 715-725页

作者： Li, Honghui Xu, Yi Peng, Yunjuan Yang, Xiaorui Zhang, Dalin Fan, Jianping Sun, Jing Beijing Jiaotong Univ Sch Comp & Informat Technol Coll Beijing Jiaotong Univ 3 Shang Yuan VillageXizhimen Wai Beijing Peoples R China Beijing Jiaotong Univ Sch Software Engn Coll Beijing Jiaotong Univ 3 Shang Yuan VillageXizhimen Wai Beijing 100091 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol 1068 Xueyuan Ave Shenzhen Peoples R China

E -governance is capable of driving the transition of government from an administrative power allocation of public resources to a citizen -service -oriented governance model. However, during this transformation, various governmental bodies often face the challenge of "data silos" caused by factors such as cross -regional, cross -business, and cross -departmental operations. Without altering the existing information platforms, transparent access technology serves as a key solution for data access within egovernance systems. It enables convenient access to information resources stored in different mediums and formats, facilitating the sharing and consolidation of information and data within and between governmental departments, thereby addressing the issue of "data silos" and enhancing the comprehensive service capabilities of e -governance. This paper firstly provides an overview of the concept, levels, characteristics, and application scenarios of transparent access to government big data. Secondly, it conducts a comprehensive comparative analysis of transparent access technologies in the context of cloud computing and big data. Lastly, based on the requirements of various transparent access technologies and the application of transparent access to government big data, this paper proposes a visionary framework for transparent access to government big data based on cross -domain semantics and channel coupling. This framework includes modules for cross -domain semantic interoperability, coupling of heterogeneous information channels based on knowledge graph, and tracing of multi -source heterogeneous data, aiming to provide innovative solutions for achieving transparent access to government big data.

关键词： cloud computing data island distributed computing government big data transparent access

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Toward a Universal Cryptographic Accelerator

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COMPUTER 2025年第1期58卷 105-108页

作者： Devadas, Srinivas Sanchez, Daniel MIT Elect Engn & Comp Sci Cambridge MA 02139 USA

Cyberphysical systems have disseminated devices that can be untrustworthy or compromised. Nevertheless, the privacy and integrity of computation and data can be guaranteed through cryptographic protocols. We address the computational burden posed by cryptography, and argue for a synergistic approach of designing programmable hardware accelerators for cryptography, followed by tailoring cryptographic protocols to this hardware.

关键词： Market Research Artificial Intelligence Computational Modeling Cloud computing Resource Management Dynamic Scheduling Decision Making Processor Scheduling distributed computing Cyber Physical Systems Resource Management Computational Resources Internet Of Things Development Of Services Application Programming Interface Functional Clusters Network Resources Cyber Physical Systems Storage Resources Edge Devices Information Technology Industry Service Components Containerized Microservices Perspective Of Migrants Artificial Intelligence Machine Learning Deep Reinforcement Learning Roadside Units ML Models

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Explicit Low-Bandwidth Evaluation Schemes for Weighted Sums of Reed-Solomon-Coded Symbols

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第8期70卷 5552-5565页

作者： Kiah, Han Mao Kim, Wilton Kruglik, Stanislav Ling, San Wang, Huaxiong Nanyang Technol Univ Sch Phys & Math Sci Singapore 637371 Singapore

Motivated by applications in distributed stor-age, distributed computing, and homomorphic secret sharing,we study communication-efficient schemes for computing linearcombinations of coded symbols. Specifically, we design low-bandwidth schemes that evaluate the weighted sum of & ell;codedsymbols in a codeword c is an element of F-n, when we are given access todof the remaining components inccc. Formally, suppose thatFis a field extension ofBof degreet. Letcccbe a codeword in aReed-Solomon code of dimensionkand our task is to computethe weighted sum of & ell;coded symbols. In this paper, for somes < t, we provide an explicit scheme that performs this taskby downloadingd(t-s)sub-symbols inBfromdavailablenodes, wheneverd >=& ell;|B|(s)-& ell;+k. In many cases, our schemeoutperforms previous schemes in the literature. Furthermore,we provide a characterization of evaluation schemes for generallinear codes. Then in the special case of Reed-Solomon codes,we use this characterization to derive a lower bound for theevaluation bandwidth.

关键词： Symbols Bandwidth Codes Maintenance engineering Servers Task analysis Reed-Solomon codes distributed storage distributed computing low-bandwidth schemes

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Incentive Design for Heterogeneous Client Selection: A Robust Federated Learning Approach

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IEEE INTERNET OF THINGS JOURNAL 2024年第4期11卷 5939-5950页

作者： Pene, Papa Liao, Weixian Yu, Wei Towson Univ Dept Comp & Informat Sci Towson MD 21252 USA

Federated learning (FL) allows the collaborative training of machine learning (ML) models between an aggregation server and different clients without sharing their private data. However, the FL archetype is mostly vulnerable to malicious model updates from various clients because of the privacy feature that makes the server see clients as a black box. When selecting clients, the server has no control on their contributions during training. This opacity of the server toward clients' data associated with the huge amount of heterogeneous data brings a security risk and poses a deterioration of the model performance in FL. The impact of client selection and data heterogeneity on FL robustness has been overlooked. In this article, we develop an incentive design for heterogeneous client selection (IHCS) to improve the performance while reducing the security risks in FL. The IHCS approach applies a smarter client selection method using cooperative game theory and dynamic clustering of clients based on their heterogeneity level to overcome the challenges of lacking access to clients' data, unbalanced data, and the lack of applicable data contribution from clients in FL. To do so, we attribute a recognition value to each client using the Shapley value. This recognition index is then used to aggregate the probability of participation level. We also implement, within the IHCS, a heterogeneity-based clustering (HIC) method that helps inhibit the negative influence of data heterogeneity and increase client contributions. Through extensive experiments with empirical results, the proposed approach outperforms the representative works on robustness of FL.

关键词： Servers Robustness Training Privacy Internet of Things Data models Security Data heterogeneity distributed computing federated learning (FL)

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Rapid 3D modelling: Clustering method based on dynamic load balancing strategy

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PHOTOGRAMMETRIC RECORD 2024年第185期39卷 67-86页

作者： Ge, Yingwei Guo, Bingxuan Xu, Guozheng Liu, Yawen Jiang, Xiao Peng, Zhe Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re 129 Luoyu Rd Wuhan 430079 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Minist Nat Resources Key Lab Urban Land Resources Monitoring & Simulat Shenzhen Peoples R China Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Xian Univ Sci & Technol Coll Geomat Xian Peoples R China Wuhan Xuntu Shikong Software Technol Co Ltd R&D Dept Wuhan Peoples R China

Three-dimensional (3D) reconstruction is a pivotal research area within computer vision and photogrammetry, offering a valuable foundation of data for the development of smart cities. However, existing methods for constructing 3D models from unmanned aerial vehicle (UAV) images often suffer from slow processing speeds and low central processing unit (CPU)/graphics processing unit (GPU) utilization rates. Furthermore, the utilization of cluster-based distributed computing for 3D modelling frequently results in inefficient resource allocation across nodes. To address these challenges, this paper presents a novel approach to 3D modelling in clusters, incorporating a dynamic load-balancing strategy. The method divides the 3D reconstruction process into multiple stages to lay the groundwork for distributing tasks across multiple nodes efficiently. Instead of traditional traversal-based communication, this approach employs gossip communication techniques to reduce the network overhead. To boost the modelling efficiency, a dynamic load-balancing strategy is introduced that prevents nodes from becoming overloaded, thus optimizing resource usage during the modelling process and alleviating resource waste issues in multidevice clusters. The experimental results indicate that in small-scale data environments, this approach improves CPU/GPU utilization by 35.8%/23.4% compared with single-machine utilization. In large-scale data environments for cluster-based 3D modelling tests, this method enhances the average efficiency by 61.4% compared with traditional 3D modelling software while maintaining a comparable model accuracy. In computer vision and photogrammetry, research enhances 3D reconstruction for smart cities. To address slow UAV-based methods, the study employs dynamic load balancing and 'gossip' communication to minimize network overhead. In small data tests, the approach improves CPU and GPU efficiency by 20.7% and 40.3%, respectively. In large data settings, it outperfo

关键词： cluster system distributed computing load balancing strategy three-dimensional reconstruction UAV images

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Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 119-137页

作者： Chellapandi, Vishnu Pandi Yuan, Liangqi Brinton, Christopher G. Zak, Stanislaw H. Wang, Ziran Purdue Univ Coll Engn W Lafayette IN 47907 USA

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future investigation to further enhance the effectiveness and efficiency of FL in the context of CAV are discussed.

关键词： Data models Surveys Federated learning Servers Training Internet of Things Data privacy Federated learning connected and automated vehicles distributed computing privacy protection data security

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A Hybrid Multi-Hop Clustering and Energy-Aware Routing Protocol for Efficient Resource Management in Renewable Energy Harvesting Wireless Sensor Networks

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IEEE ACCESS 2024年 12卷 137310-137332页

作者： Jalalinejad, Hoda Rohani Hajiabadi, Mahdi Asghar Rahmani Hosseinabadi, Ali Mirkamali, Seyedsaeid Abraham, Ajith Weber, Gerhard-Wilhelm Parikh, Jinal Islamic Azad Univ Dept Math & Comp Sci Bandar Abbas Branch Bandar Abbas *** Iran Islamic Azad Univ Fac Comp & Informat Technol Engn Qazvin Branch Qazvin *** Iran Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada Payame Noor Univ Dept Comp Engn & IT Tehran 193954697 Iran Bennett Univ Sch Artificial Intelligence Greater Noida 201310 Uttar Pradesh India Innopolis Univ Ctr Artificial Intelligence Innopolis 420500 Russia Poznan Univ Tech Fac Engn & Management PL-60965 Poznan Poland Ahmedabad Univ Amrut Mody Sch Management Technol Operat & Decis Sci Ahmadabad 380009 India

Energy Harvesting Wireless Sensor Networks (EH-WSNs) main goal is to increase efficiency in settings where Energy Harvesting (EH) is restricted by environmental resources. To solve the drawbacks of conventional Wireless Sensor Networks (WSNs) routing methods that usually ignore EH, this work presents a multi-hop clustering and renewable energy-based routing protocol designed for EH-WSNs. The suggested method performs clustering both centralized and decentralized using energy circumstances and the quantity of captured energy. The protocol operates in three phases: cluster formation, data transmission, and centralized management. To evaluate the effectiveness of the proposed approach, we analyze three distinct scenarios with different settings. The findings show that the suggested approach greatly lowers the total network energy usage while allowing a higher number of nodes to stay operational. We find that our approach outperforms AEHAC, CRBS, HUCL, and EADUC in terms of average energy levels, overall efficiency, network stability, and number of live nodes during the simulation. The results taken together show that the suggested method continuously improves network efficiency and stability in all assessed situations.

关键词： Wireless sensor networks Sensors Clustering algorithms Routing Batteries Routing protocols Renewable energy sources Energy harvesting distributed computing clustering protocol energy harvesting energy-aware distributed computing

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distributed Three-Phase Power Flow for AC/DC Hybrid Networked Microgrids Considering Converter Limiting Constraints

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IEEE TRANSACTIONS ON SMART GRID 2022年第3期13卷 1691-1708页

作者： Ju, Yuntao Liu, Wenwu Zhang, Zifeng Zhang, Ruosi China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China

In three-phase AC/DC hybrid networked microgrids (NMGs), the operational limits of AC/DC interconnected converters and distributed generator (DG) interface inverters increase the non-convexity of the power flow model, and conventional distributed power flow (DPF) algorithms based on heuristic rule may encounter convergence problems when processing limit. This paper proposed a fully DPF calculation method that can robustly handle the non-smooth reactive power limits of converters and non-smooth voltage regulation of step voltage regulators, also reducing the model dependence on the initial values. In this algorithm, the non-smooth constraints were converted into smooth functions, and based on a bi-level augmented Lagrangian alternating direction inexact Newton (ALADIN) method with the second-order convergence rate the original DPF problem was transformed into the problem of distributed step increment optimization. Accurate power flow results can be obtained by exchanging boundary information between microgrids, and the proposed algorithm can converge rapidly with step increment optimization at the second level. Numerical experiments demonstrated the accuracy, convergence, and efficiency of the proposed method.

关键词： Load flow Voltage control Microgrids Reactive power Convergence Transformers Hybrid power systems Networked microgrids distributed computing non-smooth converter limiting VSC-MTDC distributed generation privacy protection

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