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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

共 24647 条记录，以下是711-720 订阅

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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy 21

Application Research of Vertical Federated Learning Technolo...

引用

21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Yong Lu, Zhi Yin, Xiaofei Lu, Songfeng Weng, Yiting Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

ISBN: (纸本)9798350329223

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound. © 2023 IEEE.

关键词： Risk management

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

ISBN: (纸本)9789819707973

The proceedings contain 165 papers. The special focus in this conference is on Algorithms and Architectures for parallel Processing. The topics include: MDCF: Multiple Dynamic Cuckoo Filters for LSM-Tree;a Game Theory Based Task Offloading Scheme for Maximizing Social Welfare in Edge computing;research on Dos Attack Simulation and Detection in Low-Orbit Satellite Network;malware Detection Method Based on Visualization;TOC: Joint Task Offloading and Computation Reuse in Vehicular Edge computing;distributed Task Offloading for IoAV Using DDP-DQN;a Task Offloading and Resource Allocation Optimization Method in End-Edge-Cloud Orchestrated computing;multi-agent Cooperative Intrusion Detection Based on Generative Data Augmentation;an Uncertainty-Aware Auction Mechanism for Federated Learning;Long Short-Term Deterministic Policy Gradient for Joint Optimization of Computational Offloading and Resource Allocation in MEC;query Optimization Mechanism for Blockchain-Based Efficient Data Traceability;research on the Evolution Path of Network Hotspot Events Based on the Event Evolutionary Graph;Task Offloading in UAV-Assisted Vehicular Edge computing Networks;path Planning of Coastal Ships Based on Improved Hybrid A-Star;data Augmentation Method Based on Partial Noise Diffusion Strategy for One-Class Defect Detection Task;K Asynchronous Federated Learning with Cosine Similarity Based Aggregation on Non-IID Data;MPQUIC Transmission Control Strategy for SDN-Based Satellite Network;an Energy Prediction Method for Energy Harvesting Wireless Sensor with Dynamically Adjusting Weight Factor;Bayesian Optimization for Auto-tuning Convolution Neural Network on GPU;persistent Sketch: A Memory-Efficient and Robust Algorithm for Finding Top-k Persistent Flows;faCa: Fast Aware and Competition-Avoided Balancing for Data Center Network;Optimizing GNN Inference Processing on Very Long Vector Processor;GDTM: Gaussian Differential Trust Mechanism for Optimal Recommender System;privacy-Enhanced Dyna

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

ISBN: (纸本)9789819708338

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Service-Aware Cooperative Task Offloading and Scheduling in Multi-access Edge computing Empowered IoT 1

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23rd international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Chen, Zhiyan Tao, Ming Li, Xueqiang He, Ligang Dongguan Univ Technol Sch Comp Sci & Technol Dongguan Peoples R China Univ Warwick Dept Comp Sci Coventry W Midlands England

ISBN: (数字)9789819708017

ISBN: (纸本)9789819708000;9789819708017

Multi-access edge computing(MEC) enables computation task offloading and data processing at close proximity to provide rich endusers services with ultra-low latency in Internet of things(IoT). However, the high heterogeneity of the edge node configuration and the diversity of services pose challenges in fully utilizing the computing capacity in MEC. In this paper, we consider the problem of service-aware cooperative task offloading and scheduling in a three-tier MEC empowered IoT where the service requests from IoT devices can be distributed among edge nodes or further offloaded to remote cloud. As this problem is proven to be NP-hard, we proposed a two-layer Cooperative workload Initialization and Distribution Algorithm (CIDA) to solve the problem with low time complexity by decomposing it into two subproblems: 1) the optimization problem of offloading profile under dynamic resource allocation determined by the workload type, and 2) optimization problem of computation resources allocation under given offloading profile. Extensive experiments demonstrate that CIDA achieves superior performance compared to other approaches and scales well as the system size increases.

关键词： Multi-access edge computing Services-aware offloading Task scheduling Resource allocation

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

ISBN: (纸本)9789819708611

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Parareal with a Physics-Informed Neural Network as Coarse Propagator 29th

Parareal with a Physics-Informed Neural Network as Coarse Pr...

引用

29th international conference on parallel and distributed computing (Euro-Par)

作者： Ibrahim, Abdul Qadir Goetschel, Sebastian Ruprecht, Daniel Hamburg Univ Technol Inst Math Chair Computat Math Hamburg Germany

ISBN: (纸本)9783031396977;9783031396984

parallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demonstrate for the Black-Scholes equation, a partial differential equation from computational finance, that Parareal with a PINN coarse propagator provides better speedup than a numerical coarse propagator. Training and evaluating a neural network are both tasks whose computing patterns are well suited for GPUs. By contrast, mesh-based algorithms with their low computational intensity struggle to perform well. We show that moving the coarse propagator PINN to a GPU while running the numerical fine propagator on the CPU further improves Parareal's single-node performance. This suggests that integrating machine learning techniques into parallel-in-time integration methods and exploiting their differences in computing patterns might offer a way to better utilize heterogeneous architectures.

关键词： Parareal parallel-in-time integration PINN Machine learning GPUs heterogeneous architectures

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

ISBN: (纸本)9789819708581

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Low Carbon Scheduling of Thermal Power Unit Thermal Storage Capacity Based on Particle Swarm Optimization 5

Low Carbon Scheduling of Thermal Power Unit Thermal Storage ...

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5th international conference on Information Science, parallel and distributed Systems, ISPDS 2024

作者： Cao, Wenbin Wang, Mingkai Han, Bo Chai, Qi Li, Hongtao Liu, Ying State Grid Chaoyang Electric Power Supply Company China State Grid Liaoning Electric Power Supply Co. Ltd Shenyang China Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China

ISBN: (纸本)9798350352719

At present, the traditional low carbon scheduling method of thermal power unit thermal storage capacity realizes the scheduling of thermal storage capacity by establishing a thermal power coordination model. Due to the lack of analysis on the operation characteristics of thermal power units, the scheduling effect is poor and the carbon emission cost is high. Therefore, a low carbon scheduling method for thermal power unit storage capacity based on particle swarm optimization algorithm is proposed. The operation characteristics of thermal power unit are analyzed, and the pressure parameters, capacity level and fuel type of the unit are divided by the baseline. Different carbon emission allowances are allocated to the units, and the operation mechanism of the heat storage device is optimized. The low carbon scheduling model of thermal power unit thermal storage capacity is constructed and solved by using particle swarm optimization algorithm. The experimental analysis shows that the proposed low carbon scheduling method for thermal power units has good scheduling performance. © 2024 IEEE.

关键词： Carbon capture and storage

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DSUTO: Differential Rate SAC-Based UAV-Assisted Task Offloading Algorithm in Collaborative Edge computing 29

DSUTO: Differential Rate SAC-Based UAV-Assisted Task Offload...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Longxin Tan, Runti Ai, Minghui Xiang, Huazheng Peng, Cheng Zeng, Zhihao Hunan University of Technology College of Computer Science Zhuzhou China

ISBN: (纸本)9798350330717

Mobile edge computing effectively enhances service quality and decreases system cost by processing resource-intensive tasks at the network edge. Today, unmanned aerial vehicles (UAVs) are increasingly being utilized for task offloading services in remote areas due to their convenient deployment and flexible mobility. However, the complex task environment when using UAVs brings great challenges to the optimization strategy's capacity to solve and converge in a stable manner. To solve this issue, a differential rate rule (DRR) is proposed in this work with the goal of improving the update stability of the agent in the actor-critic reinforcement learning (RL). Second, a UAV-assisted task offloading algorithm called DSUTO is designed based on DRR and maximum entropy RL. Finally, a UAV-assisted mobile device-edge-cloud collaborative computing model is constructed with time-varying channel obstacles and user movement, thus solving a multi-objective joint optimization problem on the task completion cost (including delay and energy consumption) and UAV endurance under resource constraints. The experiment results demonstrate that DSUTO not only has excellent performance in terms of convergence and stability, but also significantly reduces the total system cost by 21.38% compared with the latest benchmark algorithms under complex environment conditions. © 2023 IEEE.

关键词： device-edge-cloud fusion maximum entropy reinforcement learning mobile edge computing task offloading UAV assistance

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An empirical study on the structure evolution of deep learning models: taking SAR image processing a case study 14

An empirical study on the structure evolution of deep learni...

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IEEE 14th international conference on Joint Cloud computing (JCC)

作者： Liu, Huanxi He, Xiang Feng, Dawei Bao, Han Natl Univ Def Technol Natl Lab Parallel & Distributed Proc PDL Changsha Peoples R China Natl Univ Def Technol Sch Informat & Commun Changsha Peoples R China

ISBN: (纸本)9798350328554

With the continuous improvement on model performance, deep learning models have been widely deployed and achieved promising outcomes in various fields in recent years. However, due to the escalating volumes of training data and the complexity of application problems, it becomes more and more challenging to design a neural network with better performance by hand. Analysing the evolution of typical neural network structures has important reference significance for designing a network structure. In this paper, we select the open source models in SAR image processing for an empirical analysis on the evolution of neural network structures. We analyse the evolution of 239 open source deep learning models from the aspects of framework, computing unit, model computation amount and the combined use of various computing units. Results reveal that preference and co-occurrence exist in computing units, while the average number of convolution, activation and normalization layer increases significantly over time. Model complexity shows an overall upward trend, and the characteristics of SAR image are more and more taken into consideration during the model structure design.

关键词： Intelligent model evolution Neural network structure analysis SAR image processing

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