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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

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Hybridized Shield: A Framework for Backdoor Detection in Secure Federated Learning systems 7

Hybridized Shield: A Framework for Backdoor Detection in Sec...

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7th ieee international conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Rahman, Gul Saeed-Uz-Zaman Li, Bin Muzamal, Junaid Hussain Yangzhou University School of Information Engineering Jiangsu China National University of Computer and Emerging Sciences Fast School of Computing Lahore Pakistan

ISBN: (纸本)9798350352009

In the context of Federated Learning, the vulnerability to backdoor attacks poses a significant threat to the integrity and reliability of distributed learning systems. This research introduces a novel defense framework, the' Hybridized Shield,' designed to safeguard Federated Learning environments robustly against such insidious threats. The proposed mechanism integrates Oblivious Random Grouping, Partial Parameter Disclosure, and differential outlier analysis, forming a comprehensive defense strategy that addresses the unique challenges of Federated Learning's decentralized and collaborative nature. The results reveal a significant improvement in the resilience of Federated Learning systems against backdoor attacks. Specifically, the proposed method maintains high accuracy on clean data (97.92% on MNIST and CIFAR-10) while significantly reducing the effectiveness of poisoned data attacks to a mere 10.11% accuracy, compared to the baseline Federated Learning model without defense mechanisms, which showed a 100% accuracy on poisoned data and an 18.56% attack success rate. These findings underscore the efficacy of the' Hybridized Shield' in preserving the integrity and security of Federated Learning systems, thereby contributing to the advancement of secure and reliable distributed machine learning methodologies. © 2024 ieee.

关键词： Federated learning

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DEMOTS: A Decentralized Task Scheduling Algorithm for Micro-Clouds with Dynamic Power-Budgets 16

DEMOTS: A Decentralized Task Scheduling Algorithm for Micro-...

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ieee 16th international conference on Cloud computing (ieee CLOUD)

作者： Hewage, Tharindu B. Ilager, Shashikant Rodriguez, Maria A. Arroba, Patricia Buyya, Rajkumar Univ Melbourne Cloud Comp & Distributed Syst CLOUDS Lab Sch Comp & Informat Syst Melbourne Vic Australia Vienna Univ Technol TU Wien Vienna Austria Univ Politecn Madrid Lab Sistemas Integrados LSI CCS Ctr Computat Simulat Madrid Spain

ISBN: (纸本)9798350304817

The Internet of Things (IoT) driven latency-critical applications are deployed on lightweight Micro-Clouds at the network's edge. Renting physical space from geographically distributed colocation datacenters connected via a Wide Area Network (WAN) is a cost-effective way of deploying Micro-Clouds, despite WANs' dynamic communication latency from traffic congestion. However, this deployment approach can limit Micro-Clouds to operate within a soft power budget, as colocation datacenter providers utilize it to add more servers and lower capital costs through oversubscribing power infrastructure. As a result, Micro-Clouds use extreme energy reduction measures like power capping and task throttling to address power overdraw events, where power consumption exceeds soft power budget limits, which reduces the performance of latency-critical applications. We propose a solution where a dynamic power budget can be achieved by adding renewable energy sources to the existing soft power budget without upgrading power delivery systems. To take advantage of this, we propose a dynamic, decentralized task-scheduling algorithm called DEMOTS. DEMOTS effectively utilizes the available dynamic power budget in a WAN with varying degrees of network traffic congestion, thereby avoiding the need for extreme energy reduction measures. We implement DEMOTS on a simulation test-bed. Compared to state-of-the-art baseline using MCOP for decentralized task-scheduling in Micro-Clouds, DEMOTS reduces Power Overdraw Impact up to 19%, Task Latency Increase Impact up to 47%, and Task Schedule Time Impact up to 49%.

关键词： Decentralized Resource Management Micro-Clouds Task Scheduling Energy Efficient computing

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About Granular Rough computing: Concept-Dependent Granulation Powered by Map Reduce

About Granular Rough Computing: Concept-Dependent Granulatio...

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2024 ieee international conference on Big Data, BigData 2024

作者： Cybulski, Radoslaw University of Warmia and Mazury in Olsztyn ul. Sloneczna 54 Olsztyn10-710 Poland

ISBN: (纸本)9798350362480

This work continues a cycle of research grounded in the foundational theories of granular computing introduced by Zadeh, rough set theory proposed by Pawlak, and particularly the concept-dependent granulation methodology developed within Polkowski's theoretical framework. Building on our prior studies, we advance the application of granulation methods for knowledge extraction from decision-making systems. This approach forms knowledge granules - clusters of similar objects based on selected measures - allowing us to structure the universe of objects into prototypical representations that capture recurring data patterns. Such representations have demonstrated high efficacy in classification tasks, preserving data accuracy while achieving substantial reductions - up to 98% - in the size of training systems. In this paper, we further test the scalability of this method in large-scale data processing by employing MapReduce within the Apache Hadoop ecosystem, enhancing computational efficiency through distributed execution in a Java-based environment. © 2024 ieee.

关键词： MapReduce

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Research on distributed Mobile Cloud Cooperation Architecture Using AI Control Software and Hardware 4

Research on Distributed Mobile Cloud Cooperation Architectur...

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4th ieee international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Zhang, Feng Cui, Yunkun College of Physical Education Taishan University Tai'an271000 China

ISBN: (纸本)9798350359589

This paper studies the network based distributed mobile cloud cooperation architecture and QoS guarantee mechanism of end-to-end computing and control. By implanting cloud computing resources in the mobile communication network, a near-end low-delay cloud service is formed. This project intends to study the mobile cloud cooperative controller. The control strategy of collaboration with terminal, mobile network and distributed cloud server is studied. This structure can reduce the user's interaction of trust instructions between nodes in the computing process, and reduce the CPU usage and energy consumption of nodes. The distributed mobile cloud computing cooperative controller is introduced, and the perception and computing control decision-making mechanism based on the coordination between terminal, mobile network and distributed cloud server is introduced. This project will provide guarantee for end-to-end mobile cloud services. The experimental simulation proves that this method has good scalability. The simulation results show that the framework has good scalability. It can quickly fix anomalies in collaboration services when they occur. The designed framework has good practicability. © 2024 ieee.

关键词： AI control collaborative architecture delay intelligent terminal synergy

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Advancing Scalability and Efficiency in distributed Network computing Through Innovative Resource Allocation and Load Balancing Strategies

Advancing Scalability and Efficiency in Distributed Network ...

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international conference on Intelligent and Fuzzy systems (INFUS)

作者： Singh, Manisha Bhardwaj, Purvee Bhardwaj, Ramakant Narayan, Satyendra CKEM Coll Dept Comp Sci Satna MP India Rabindranath Tagore Univ Dept Phys Sci Bhopal India Amity Univ Dept Math Kolkata WB India Sheridan Inst Technol Dept Appl Comp Oakville ON Canada

ISBN: (纸本)9783031671944;9783031671951

This research aims to explore and develop original approaches for improving the scalability and efficiency of distributed network computing systems. The escalating demand for high-performance computing and the widespread integration of interconnected devices present a critical challenge in optimizing resource allocation and load balancing within distributed networks. The study will investigate cutting-edge algorithms, employ machine learning techniques, and devise adaptive strategies to dynamically distribute computing tasks across network nodes. The primary objective is to enhance system scalability, minimize response times, and maximize resource utilization, contributing significantly to the progression of network technologies in distributed computing environments. The research findings are expected to have substantial implications for various applications, including cloud computing, edge computing, and Internet of Things (IoT) ecosystems.

关键词： Edge computing Internet of Things (IoT) Cloud computing Digital Ecosystems Challenges and Solutions Responsible Technology Evolution

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SmartDefend - IoT Security Using Machine Learning 8

SmartDefend - IoT Security Using Machine Learning

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8th ieee international conference on distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Saumya, Y.M. Vinay, P. Pinto, Charis Correia, Natasha Elizabeth Miranda, Melanie Crystal Dsouza, Joyline Rencita St Joseph Engineering College Dept. of CSE Vamanjoor India Canara Engineering College Dept. of CSE Bantwal India

ISBN: (纸本)9798350350593

With the advent of the fourth industrial revolution in recent years, technological advancements have led to massive exponential growth in the Internet of Things (loTs), fog computing, computer security, and cyberattacks. One of the most promising techniques for mitigating cybersecurity risks and ensuring security is machine learning, or ML. In order to address the current security and privacy issues, a number of research have suggested smart intrusion detection systems (IDS) using intelligent architectural frameworks that use AI. This paper presents a systematic literature review (SLR) that maps, surveys, and categorises the body of research on machine learning techniques for identifying cybersecurity threats in Internet of Things environments. Notably, random forests (RF) and support vector machines (SVM) are two of the most popular techniques because of their effective memory usage and excellent detection accuracy. The system aims not only to detect anomalies but to delve deeper, providing detailed insights through the precise classification of detected anomalies into specific attack types. Among the seven algorithms examined, the Random Forest Classifier yields the best accuracy of 92.72% in binary classification and 92.40% in multiclass classification. © 2024 ieee.

关键词： Fog computing

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Fault-Tolerant Deep Learning Cache with Hash Ring for Load Balancing in HPC systems

Fault-Tolerant Deep Learning Cache with Hash Ring for Load B...

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Lee, Seoyeong Khan, Awais Kim, Yoochan Park, Junghwan Hwang, Soon Lee, Jae-Kook Hong, Taeyoung Zimmer, Christopher Kim, Youngjae Sogang University Korea Republic of Oak Ridge National Laboratory United States KISTI Korea Republic of

ISBN: (纸本)9798350355543

Large-scale DL on HPC systems like Frontier and Summit uses distributed node-local caching to address scalability and performance challenges. However, as these systems grow more complex, the risk of node failures increases, and current caching approaches lack fault tolerance, jeopardizing large-scale training jobs. We analyzed six months of SLURM job logs from Frontier and found that over 30% of jobs failed after an average of 75 minutes. To address this, we propose fault-tolerance strategies that recache data lost from failed nodes using a hash ring technique for balanced data recaching in the distributed node-local caching, reducing reliance on the PFS. Our extensive evaluations on Frontier showed that the hash ring-based recaching approach reduced training time by approximately 25% compared to the approach that redirects I/O to the PFS after node failures and demonstrated effective load balancing of training data across nodes. © 2024 ieee.

关键词： distributed Deep Learning Fault Tolerance HPC NVMe Cache

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TGCN-DA: A Temporal Graph Convolutional Network with Data Augmentation for High Accuracy Insider Threat Detection 22

TGCN-DA: A Temporal Graph Convolutional Network with Data Au...

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ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Li, Ximing Li, Linghui Li, Xiaoyong Cai, Binsi Li, Bingyu Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Minist Educ Beijing Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Insider threats present a formidable challenge to cybersecurity, as insiders possess the privileges and information necessary to execute diverse attacks. A comprehensive analysis of user behavior, including behavioral features, sequences, and inter-user relationships, is required for effective insider threat detection. However, few existing methods consider these features in an integrated manner, which could result in high false positives. To further improve the accuracy of insider threat detection, we propose a novel framework for insider threat detection based on a temporal graph convolutional network with data augmentation (referred to as TGCN-DA), which integrates the exploration of structural information among users and simultaneously captures the behavior temporal dependencies. In particular, we introduce an edge predictor to encode user structural information and strengthen intra-class edges among users based on the representation of users' behavior. Additionally, the GCN with temporal feature mechanism is leveraged to learn dynamic changes in users' behavior to capture behavior temporal dependence. Extensive experiments demonstrate that our proposed TGCN-DA outperforms other state-of-the-art methods and achieves higher accuracy in the task of insider threat detection.

关键词： Insider Threat Detection Anomaly Detection Graph Neural Network

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N4: Network for N Neural Network Training 59

N4: Network for N Neural Network Training

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59th Annual ieee international conference on Communications (ieee ICC)

作者： Zhou, Jiasheng Lin, Shengrui Liu, Hongyan Chen, Xinyang Shi, Pengpai Zhu, Longlong Zhang, Dong Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China Fuzhou Univ Zhicheng Coll Fuzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9781728190549

As the amount of data and complexity of neural network models continue to grow, distributed training has become increasingly crucial for improving training speed. However, the bottleneck of distributed training is the communication overheads among distributed workers. Recent research has shown that performing in-network aggregation using programmable switches is a good way to accelerate distributed training. However, previous work has only targeted specific neural network models and can only be applied in specified network topologies. Administrators may train different models and train them in different network topologies. In order to generalize the approach of using programmable switches to accelerate distributed training, we propose N4, a programmable intra-switch acceleration framework that supports distributed training of multiple neural networks. N4 also realizes the deployment of distributed workers based on any topology. Our experimental results show that N4 ensures high performance and isolation when training numerous neural networks. N4 outperforms state-of-the-art systems, accelerating training for existing methods by up to 3.4x.

关键词： distributed training Programmable switches Greedy algorithms ILP

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Research on Autonomous Control Strategy for Safe Operation of High Proportion distributed PV Stations 6

Research on Autonomous Control Strategy for Safe Operation o...

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6th ieee international conference on Power, Intelligent computing and systems, ICPICS 2024

作者： Li, Guowu Li, Weiyu Yi, Shuxian Liu, Shen Ma, Xinsheng Liu, Zhenyu Pei, Xiaoliang State Grid Jibei Electric Power Company Limited Distribution Network Management Department Beijing China Electric Power Research Institute State Grid Jibei Electric Power Company Limited Beijing China Qingdao Dingxin Communication Co. Ltd Beijing-Tianjin-Hebei Marketing Service Center Shandong Qingdao China

ISBN: (纸本)9798350374315

In recent years, with the progress of distributed photovoltaic technology and the reduction of costs, the installed scale of distributed photovoltaic has grown rapidly. Due to the high average photovoltaic permeability, there is active power backfeed phenomenon in the distribution network at the peak of photovoltaic power generation, resulting in high voltage and distribution overload problems in the station area, especially the serious overvoltage of photovoltaic access points, which has seriously affected the reliable operation of the distribution network and the safe consumption of electricity by users. At present, there is little attention to the regulation of distributed PV access point overvoltage at home and abroad, and the related work has just started, and the relevant achievements are also few, so it is urgent to carry out the relevant research on the autonomous control strategy for the safe operation of distributed power station area. This paper proposes an autonomous control strategy for safe operation of distributed power stations. Firstly, the hardware architecture of 'distribution automation master station-intelligent fusion terminal-intelligent grid-connected switch/plug and play communication unit-photovoltaic inverter' is constructed. Then, based on the above hardware architecture, a refined control strategy is proposed. Finally, based on RTDS, a semi-physical simulation platform of intelligent fusion terminal is built, and the effectiveness and practicability of the method described in this paper are verified by a practical example. © 2024 ieee.

关键词： autonomous region distributed power supply Photovoltaic control Safe operation Semi-physical simulation

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