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distributed Intelligent Framework for Remote Area Observation on Multilayer Non-Terrestrial Networks

Distributed Intelligent Framework for Remote Area Observatio...

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IEEE international Mediterranean conference on Communications and networking (IEEE MeditCom)

作者： Naseh, David Shindet, Swapnil Sadashiv Tarchi, Daniele DeColat, Tomaso Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marcon I-40136 Bologna Italy Univ Bologna CNIT Res Unit Bologna Italy German Aerosp Ctr DLR Inst Commun & Nav Wessling Germany

ISBN: (纸本)9798350309492;9798350309485

Satellite-based Remote Area Observation systems are becoming increasingly popular in the upcoming 6G world. However, traditional Earth Observation (EO) systems suffer from communication requirements, reliability, and data privacy issues. To address these issues, we propose a multilayered Non-Terrestrial Network (NTN) based EO framework for remote area observation purposes. The proposed framework includes the air network along with traditional satellite networks for reliable and low-cost EO services. Additionally, with onboard edge computing facilities, the proposed EO framework can process data in space. Next, given the importance of intelligent services in the 6G world, we extend the multi-layered EO framework and propose a novel distributed Learning (DL) solution for federated training. The proposed framework is defined as Generalized Federated Split Transfer Learning (GFSTL), which can induce split and transfer learning tools into a federated learning framework for improving overall training performance and accuracy. Moreover, GFSTL uses Unmanned Aerial Vehicles (UAVs) for improved data accuracy and image quality in challenging terrains, ensuring increased accuracy in EO applications, and establishes a resilient model for efficient and secure training across distributed platforms, making it both efficient and accurate. In addition, SL helps resource-constrained UAVs perform the task efficiently, enhancing scalability and extensibility. Finally, we conduct experiments to provide theoretical and numerical insight into the performance of the proposed method.

关键词： distributed Learning Federated Learning Transfer Learning Split Learning Earth Observation Non-Terrestrial Networks

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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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Multi-Terminal Collaborative distributed Garden Planning System Integrating CG Technology 3

Multi-Terminal Collaborative Distributed Garden Planning Sys...

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3rd international conference on Pervasive computing and Social networking, ICPCSN 2023

作者： Wang, Ye Liu, Qi School of Art Design and Media Sanda University Shanghai201209 China

ISBN: (纸本)9798350322842

The fast distributed garden planning system in colleges and universities is one of the mainstream applications of contemporary computers. The latest traditional methods usually use the GPU full virtualization technology as an entry point to improve the problem of a single user and a single computer, so that a physical computer can shared by multiple users without interfering with each other. With this basis, this research study designs the novel multi-terminal collaborative distributed garden planning system integrating CG technology. Key innovations are: (1) The parallel SSOR-AI preconditioning combined with the CG algorithm is proposed to improve the traditional CG model. (2) The designed multi-terminal collaboration model considers the heterogeneous structure that will be more robust for the complex data. (3) The virtual image model is designed to finish the whole work flow. In the experiment part, the data is collected from Baidu search engine with the size of more than 1000000, the various tests are conducted to validate the performance. © 2023 IEEE.

关键词： Search engines

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VersatileFL: Volatility-Resilient Federated Learning in Wireless Edge Networks 20

VersatileFL: Volatility-Resilient Federated Learning in Wire...

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20th Annual IEEE international conference on Sensing, Communication, and networking (SECON)

作者： Yoon, JinYi Kim, Jeewoon Byeon, Yeongsin Lee, HyungJune Ewha Womans Univ Dept Comp Sci & Engn Seoul South Korea Univ Calif Irvine Dept Comp Sci Irvine CA USA Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9798350300529

In the era of artificial intelligence (AI), deep neural networks (DNNs) become larger using a massive amount of data, and thus, they are trained via cooperative computing devices (e.g., GPUs or servers) based on federated learning. As computation and data generation move to the edge due to privacy, latency, or bandwidth issue, DNN with edge devices has been investigated. However, edge devices are wirelessly connected and mostly incur fragile connectivity. We propose VersatileFL, a novel volatility-resilient deep learning framework under hostile environments. We address short-term and long-term volatility: 1) versatile distributed learning against short-term fluctuation by substituting the missing intermediate values with the past or approximated values and 2) model rearrangement with runtime connectivity diagnosis against long-term variation by adaptively adjusting the partitioned model for the impaired. We have demonstrated that VersatileFL has achieved 62.0 % and 31.9 % higher performance than hostile learning without a maintenance scheme against the short-term and long-term volatility, respectively.

关键词： Federated Learning distributed Learning Split Learning Model Parallelism Wireless Networks Network Volatility Edge Intelligence

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Votereum: Blockchain based Secure Voting System 4

Votereum: Blockchain based Secure Voting System

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4th international conference on Pervasive computing and Social networking (ICPCSN)

作者： Kumari, Divya Veni, Nitish Kumar, Pranith M. Manoj, H. Purohit, Hrishikesh New Horizon Coll Engn Fac Informat Sci & Engn Bangalore Karnataka India New Horizon Coll Engn Informat Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9798350386356;9798350386349

In an era where trust and transparency are paramount in electoral processes, this project introduces a novel voting system leveraging blockchain technology. The proposed system aims to address inherent challenges in traditional voting methods, such as security vulnerabilities, lack of transparency, and the potential for tampering. By implementing a decentralized approach using blockchain, the project ensures the integrity and confidentiality of the voting process while enhancing accessibility and trust. The key components of the system include smart contracts, cryptographic techniques, and a user-friendly interface. Smart contracts, deployed on a chosen blockchain platform, define the rules and logic governing the voting process, ensuring transparency and accountability. Cryptographic methods are employed to secure voter authentication, ballot creation, and result verification, safeguarding against unauthorized access and tampering. The user interface is designed to be intuitive, facilitating a seamless and secure voting experience. The system encompasses features such as secure voter authentication, unique and verifiable ballot generation, and a transparent consensus mechanism. Through these elements, the project aims to pre vent double voting, uphold voter pri vacy, and ensure the accuracy of election results. Additionally, collaboration with election authorities and adherence to legal and compliance standards contribute to the credibility of the system. Thorough testing, including security audits and simulated voting scenarios, is conducted to identify and rectify potential vulnerabilities. The scalability of the system is considered to accommodate a large number of voters, making it suitable for various election scales. Continuous updates and community engagement further enhance the system's robustness and resilience. This blockchain-based voting system not only meets the functional requirements of a secure and transparent electoral process but also addresses th

关键词： Metamask Ganache Ethereum distributed System Blockchain Technology Ethereum Crypto Ethereum Wallets Contracts Blockchain Smart Contract distributed General Ledger Smart Contracts liquid democracy Decentralized system Temper proof Transparent

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Detection and Prevention Mechanism for DDoS Attacks in Cloud computing:The Role of Software-Defined networking(SDN) 5

Detection and Prevention Mechanism for DDoS Attacks in Cloud...

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5th IEEE international conference on Advances in computing, Communication Control and networking, ICAC3N 2023

作者： Shilpa Dahiya, Mamta Virmani, Charu Mriirs Computer Science And Engineering Faridabad India Mriirs Computer Science Engineering School Of Engineering And Technology Faridabad India

ISBN: (纸本)9798350330861

Cloud computing has developed as a vital technology for offering on-demand computing resources that meet the changing requirements of modern applications. The cloud environment, on the other hand, is especially vulnerable to distributed Denial of Service (DDoS) assaults, which can interrupt services and cause serious financial and reputational harm. The purpose of this research paper is to investigate the detection and prevention techniques for DDoS attacks in a cloud computing environment, with a special emphasis on the function of Software-Defined networking (SDN).This paper present an overview of cloud infrastructure, its importance in today's world, and the inherent weaknesses that make it an appealing target for attackers. Furthermore, it cover numerous sorts of DDoS attacks that might jeopardize cloud service availability and performance. The paper also explores the advantages SDN offers to cloud security by utilizing the dynamic and centralized control it provides. Through flow monitoring and analysis, behavioral anomaly detection, and machine learning-based approaches, the potential of SDN to improve the detection of DDoS attacks is explored. The SDN-enabled preventative techniques, such as traffic engineering, rate limitation, access control, and authentication procedures, as well as the significance of service provider cooperation in effectively mitigating DDoS assaults, are also discussed. © 2023 IEEE.

关键词： Software defined networking

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The Anatomy of an Infrastructure for Digital Underground Mining 8

The Anatomy of an Infrastructure for Digital Underground Min...

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8th international conference on Internet of Things, Big Data and Security (IoTBDS)

作者： Sreedharan, Sreekant Ramachandran, Muthu Ghosh, Soma Prakash, Suraj IIT ISM Dhanbad TEXMiN Fdn 3rd Floor CRE Bldg Dhanbad India Univ Southampton Dept Comp Sci Southampton England JPMorgan Chase & Co Engn & Architecture AI ML Bangalore India

ISBN: (纸本)9789897586439

Over 41.6 billion IoT devices are expected to come online by 2025, collectively capable of generating 80 zettabytes (ZB) of data. Despite the relentless progress in adoption of smart devices occurring around all us in our smart homes, our wearable devices, our smart cities and workplaces -, progress in the adoption of smart technology in the mining sector has languished. Yet the mining sector powers our energy systems and makes components for smart devices possible, while employing over 4.5 million people world-wide in some of the most extreme & hostile environments. This paper presents the design of a prototype industrial IoT platform for large-scale industrial automation of conventional mines.

关键词： Architecture Industrial IoT Mining4.0 Edge computing distributed computing Smart Infrastructure Private Cloud computing Real-Time Operating Systems Wireless Mesh networking Wearable computing distributed Automation

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EUDRL: Explainable Uncertainty-Based Deep Reinforcement Learning for Portfolio Management 15

EUDRL: Explainable Uncertainty-Based Deep Reinforcement Lear...

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15th international conference on computing Communication and networking Technologies, ICCCNT 2024

作者： Lina, Jia Banda, Shahab S. Paib, Hao-Ting Rawal, Bharat S. International Graduate School of Artificial Intelligence Yunlin County Douliu City Taiwan Bachelor Program of Big Data Applications National Pingtung University Pingtung Taiwan Department of Computer Science and Digital Technologies University GgramblingLA United States

ISBN: (纸本)9798350370249

Supervised deep learning (SDL) has shown remarkable success in various financial applications, such as stock prediction and fraud detection. However, SDL's reliance on class labels renders it unsuitable for portfolio management (PM) tasks, where such labels are often unavailable. To address this limitation, we propose a novel two-level architecture based on deep reinforcement learning (DRL) for PM, which does not require class labels. Our approach comprises several local agents that provide trading decisions and uncertainty assessments for individual stocks, and a global agent that makes portfolio management decisions based on the outputs of the local agents. Additionally, we incorporate the concept of explainable AI (XAI) into our framework using the SHAP (Shapley additive explanations) method, enhancing the transparency and interpretability of the global agent's decisions. Our experimental results demonstrate that the proposed architecture consistently yields profitable outcomes in the market. ©2024 IEEE.

关键词： Deep reinforcement learning

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Synergy: Collaborating Centralized and Local Scheduling for Serverless Functions 30

Synergy: Collaborating Centralized and Local Scheduling for ...

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30th IEEE international conference on Parallel and distributed Systems, ICPADS 2024

作者： Chen, Hanmei Zhao, Laiping Yang, Yanan You, Jianing Li, Keqiu Tianjin University Tianjin Key Lab. of Advanced Networking Tianjin China Tianjin University Tanklab Tianjin China Cloud Computing Research Institute China Telecom Beijing China

ISBN: (纸本)9798331515966

Serverless computing enables a new way of building and scaling cloud applications by allowing developers to write fine-grained functions. The execution duration of a cloud function is typically short, usually ranging from a few milliseconds to a few seconds. FaaS providers charge users based on the execution duration of cloud functions with a granularity of 1 millisecond. Existing mixed scheduling methods collocate functions with varying execution times, which may prolong their execution duration and lead to unfair charges for FaaS users. To address this problem, we propose a partition scheduling approach, placing functions with varying execution times on different partitions. We introduce Synergy, a solution in serverless computing that leverages a collaboration of central and local scheduling to partition functions and employs suitable scheduling algorithms for these partitions respectively. Synergy also supports the dynamic switching of scheduling algorithms for partitioned nodes to adapt to highly fluctuating loads. We evaluate Synergy using real-world, representative benchmarks. Experimental results demonstrate that, compared to state-of-the-art and conventional approaches, Synergy can reduce the average function execution duration by 63%. © 2024 IEEE.

关键词： Scheduling algorithms

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PE-FedAvg: A Privacy-Enhanced Federated Learning for distributed Android Malware Detection 21

PE-FedAvg: A Privacy-Enhanced Federated Learning for Distrib...

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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： Tang, Junwei Xu, Zilong Ye, Luyao Peng, Tao He, Ruhan Hu, Xinrong Tian, Wenlong Wuhan Textile University Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion China Wuhan Textile University School of Computer Science and Artificial Intelligence China University of South China School of Computer Science China Nanyang Technological University School of Physical and Mathematical Sciences Singapore

ISBN: (纸本)9798350329223

Android malware detection has become research hotspot in mobile security. When security service providers obtain feature information from target samples, they may involve user privacy information such as identity and behavioral habits, creating an urgent need for a distributed machine learning model to carry out malicious application detection with privacy protection. However, the information exchange between clients and servers in basic federated learning still poses risks of model data leakage, and the likelihood of inferring privacy information from the original feature data is high. Therefore, This paper propose a privacy-enhanced federated learning system called PE-FedAvg for distributed Android malware detection. Firstly, Gaussian noise is added to the feature data uploaded from the client to the server, satisfying differential privacy characteristics while retaining data distribution for privacy protection. Secondly, an improved homomorphic encryption algorithm based on modular repeated squaring is used to encrypt and transmit the gradient weight information exchanged between federated learning clients and servers, preventing model-targeted attacks. By conducting the experiments on the Drebin and Malgenome datasets, our design has demonstrated exceptional performance compared to the state-of-the-art work, achieving accuracy rates of 98.61% and 99.3%, respectively. This achievement highlights that our approach not only enhances the security of the federated learning system, but also more effectively accomplishes the detection of Android malware. © 2023 IEEE.

关键词： Android malware

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