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Efficient Large Models Fine-tuning on Commodity Servers via Memory-balanced Pipeline Parallelism 25

Efficient Large Models Fine-tuning on Commodity Servers via ...

引用

25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Liu, Yujie Lai, Zhiquan Liu, Weijie Wang, Wei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

Large models have achieved impressive performance in many downstream tasks. Using pipeline parallelism to fine-tune large models on commodity GPU servers is an important way to make the excellent performance of large models available to the general public. Previous solutions fail to achieve an efficient memory-balanced pipeline parallelism. In this poster, we introduce a memory load-balanced pipeline parallel solution. this solution balances memory consumption across stages on commodity GPU servers via NVLink bridges. It establishes a new pathway to offload data from GPU to CPU by using the PCIe link of adjacent GPUs connected by the NVLink bridge. Furthermore, our method orchestrates offload operations to minimize the offload latency during large model fine-tuning. Experiments demonstrate that our solution can balance the memory footprint among pipeline stages without sacrificing training performance. © 2023 ieee.

关键词： Program processors

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Reactive Power Optimization in Distribution Network with Renewable distributed Power Sources 4

Reactive Power Optimization in Distribution Network with Ren...

引用

4th international conference on Intelligent Power and systems, ICIPS 2024

作者： Zhang, Yufan Pan, Aiqiang Xiong, Xuejun Zhang, Yajun Liu, Hanxiao Li, Liwei Shanghai Municipal Electric Power Company State Grid Shanghai China Shandong University College of Computer Science and Technology Qingdao China

ISBN: (纸本)9798331532765

In order to solve the problems of reactive power optimization in distribution networks which includes the high proportion of distributed new energy power generation, this paper proposes a multi time scale reactive power optimization method. Firstly, on the day-ahead scale, the slow motion control variables were optimized with the objective function of minimizing the expected network loss, and a master-slave classification period decoupling strategy was proposed to transform the dynamic optimization of discrete variables into Static optimization to simplify the optimization process;Secondly, on the 15 minute scale, rolling optimization of reactive power output of new energy is adopted, with the goal of minimizing the system voltage deviations;Finally, in real time, reactive power output of new energy is optimized at any time with the goal of maintaining a constant voltage at the grid connection point within each time window to suppress voltage stability issues caused by various random fluctuations. Finally, the effectiveness and feasibility of the proposed method were verified using the modified ieee33 node as an example. © 2024 ieee.

关键词： Power distribution networks

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Artificial Intelligence in American Agriculture: A Comprehensive Review of Spatial Analysis and Precision Farming for Sustainability

Artificial Intelligence in American Agriculture: A Comprehen...

引用

2024 ieee international conference on computing, Applications and systems, COMPAS 2024

作者： Akter, Jahanara Kamruzzaman, Md Hasan, Rakibul Khatoon, Rabeya Farabi, Syeda Farjana Ullah, Md Wali Westcliff University Department of Business Administration 17877 Von Karman Ave 4th Floor IrvineCA92614 United States International American University Department of Business Administration 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States

ISBN: (纸本)9798331529765

Artificial Intelligence (AI) is revolutionizing the agricultural sector by enhancing precision farming and spatial analysis, particularly within the diverse agro-ecological zones of USA. this study investigates the transformative potential of AI technologies, such as sensor-based monitoring and satellite imaging analysis, in improving crop yields, soil health, and climate resilience. Despite the opportunities presented by AI in agriculture, such as increased efficiency and sustainability, the African agricultural landscape also faces significant challenges, including infrastructural limitations and socio-economic disparities. the research delves into how AI can address these challenges by promoting economic growth and sustainable development. Moreover, it highlights the critical role of AI in precise crop monitoring, soil health assessment, and decision-making through advanced weather forecasting. By examining the socio-economic effects of AI adoption, this study underscores the necessity of supportive policies and best practices to harness AI's full potential in fostering resilient and sustainable farming methods in USA. through comprehensive analysis and strategic recommendations, this research contributes to the broader understanding of AI's impact on American agriculture, paving the way for innovative and effective agricultural practices. © 2024 ieee.

关键词： Sustainable agriculture

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Performance Analysis of Aggregation Algorithms in Cross-Silo Federated Learning for Non-IID Data 4

Performance Analysis of Aggregation Algorithms in Cross-Silo...

引用

4th international conference on Embedded and distributed systems, EDiS 2024

作者： Delehouzée, Mathis Lessage, Xavier Reginster, théo Mahmoudi, Saïd Faculty of Engineering - ILIA/Infortech University of Mons Rue de Houdain 9 Mons7000 Belgium Cetic Applied Research Center Av. Jean Mermoz 28 Charleroi6041 Belgium

ISBN: (纸本)9798331519872

Federated learning is a powerful machine learning paradigm that enables a large number of machine learning applications that must comply with strict and complex data privacy regulations. In this paper, we focus on one of the fundamental components of Federated Learning: federated aggregation algorithms. these algorithms play a pivotal role in consolidating insights and the model updates from various clients while preserving data privacy and security. We compare their efficacy across different types of dataset configurations, including balanced IID (Independent and identically distributed) data, unbalanced IID data, and non-IID data characterized by label distribution skew and feature distribution skew. For our specific use case, the experiments presented in this work show that FedProx is the overall best performing state-of-the-art algorithm for binary classification tasks of medical X-rays distributed in datasets of a small number of hospitals. © 2024 ieee.

关键词： Federated learning

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Application of time-space correlation correction model in ultra-short-term distributed photovoltaic power prediction 4

Application of time-space correlation correction model in ul...

引用

4th international conference on Intelligent Power and systems, ICIPS 2024

作者： Liu, Baoyong Wang, Yuming Hou, Zhen Shushpanov, Ilia Suslov, Konstantin Shi, Jie Shandong Electric Power Engineering Consulting Institute Jinan China University of Jinan Jinan China Irkutsk National Research Technical University Irkutsk Russia Moscow Power Engineering Institute Moscow Russia

ISBN: (纸本)9798331532765

Reliable distributed photovoltaic (PV) power prediction is essential for grid managers to obtain output data and mitigate effects. An advanced model that incorporates AI and the concept of time-space correlation is introduced in this paper, addressing both the current neural network algorithms' imprecision in prediction and the insufficient investigation into the time-space correlation of stations. Firstly, a Bayesian-optimized LSTM is employed to construct a model specifically designed. this approach leverages the Bayesian optimizer to mitigate overfitting issues within the model. Secondly, based on the above prediction model, a time-space correlation correction model is proposed by resolving the intra-day multi-scale time coupling of PV power and the multiple space coupling of centralized-distributed sites. Finally, the LSTM prediction model, augmented by the time-space correlation correction model, has been demonstrated to be more accurate in predicting than other models. the MAE was enhanced by 6.38%, RMSE by 5.58%, and MAPE by 4.9% when the algorithm and model were implemented in a distributed PV power plant in Australia, as evidenced by the case study results. © 2024 ieee.

关键词： Prediction models

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RAW Optimization of ieee 802.11ah Networks 4

RAW Optimization of IEEE 802.11ah Networks

引用

4th international conference on computing and Communication systems, I3CS 2023

作者： Mondal, Md. Arifuzzaman Khongjoh, Saradaphisha Hussain, Md. Iftekhar North-Eastern Hill University Department of Information Technology Shillong India

ISBN: (纸本)9798350323771

ieee 802.11ah (also known as WiFi Halow) is a new WiFi standard for sub-l GHz communications intended to address key challenges of the Internet of things. ieee 802 working group introduces ieee 802.11ah to overcome the disadvantages of various previously introduced protocols (more data rate and very less transmission range, and more transmission range and very less data rate) by providing much data rate and transmission range of up to 1 km and has the capacity of 8192 devices connecting simultaneously with only one access point. the ieee 802.11ah standard was released to support Machine-to-Machine (M2M) Communication between a large number of sensor devices that are rapidly increasing with the Internet of things (IoT). ieee 802.11ah protocol introduces the RAW Mechanisms in the MAC layer to avoid channel contention, especially when there is a large number of stations trying to access the channel simultaneously, thereby increasing scalability in dense deployments. In this paper, we analyze the network performance in terms of throughput through the proper allocation of nodes. this article investigates the impact of the number of stations and the number of groups on network performance, specifically throughput, in wireless networks. the article examines how varying the number of stations and groups affects the network's performance and identifies the optimal values for these parameters that can maximize the network's throughput. © 2023 ieee.

关键词： Network performance

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IoT Security Enhancements in Smart Healthcare Using Federated Learning 4

IoT Security Enhancements in Smart Healthcare Using Federate...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Lanka, Surekha Moodhitaporn, Taipida Director of BSc-IT Program Stamford International University Thailand Stamford International University Department of Creative Media and Design Bangkok Thailand

ISBN: (纸本)9798331523923

Federated learning (FL) is a distributive machine learning (ML) approach that makes use of a centralised server to assist several Internet of things (IoT) devices in cooperatively training an ML model. IoT device local data is safeguarded since it never leaves the device in FL. Since distributed IoT devices in FL often gather their local data on their own, each device's data set may naturally constitute a unique source domain. In this research work, there will be maintaining the security enhancements for smart healthcare using federated learning. there will be securing the data of IoT -based sensors which may lose local data while using the smart systems. Removing this issue will give importance to local data also using the FL method. thus, this research involves considering different research work and gives the research methodology for the security-enhancing process using FL in IoT enhanced smart healthcare. © 2025 ieee.

关键词： Federated learning

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Cloud-Connected Wearable Devices for Lone Worker Protection With Wireless sensor Networks 4

Cloud-Connected Wearable Devices for Lone Worker Protection ...

引用

4th international conference on Sustainable Expert systems, ICSES 2024

作者： Mohankumar, N. Manivannan, K.K. Babu, A. Ramesh Sampoornam, M. Maria Kovarasan, Rajesh Kambattan Srinivasan, C. Nagpur Campus Maharashtra Pune India Kcg College of Technology Department of Mechatronics Engineering Tamil Nadu Chennai India School of Computer Applications Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Various sectors continue to prioritize lone worker safety, which requires creative ways to provide quick support and monitoring. To safeguard lone workers, this paper introduces a new method that makes use of cloud-connected wearable devices that are coupled with Wireless sensor Networks (WSNs). the system design relies on sensors embedded in wearable devices that can monitor a range of environmental elements as well as the vital signs of workers. A central cloud platform receives data in real-time via wireless communication from these devices and processes and analyzes it. the system may detect possible dangers or crises using smart techniques and immediately notify those who are supposed to react or the proper authorities. Cloud computing also makes it easy to store, analyze, and monitor large amounts of data over time, which is great for doing proactive risk assessments and improving safety standards. It demonstrates solution's capacity to improve lone worker safety, reduce reaction times, and mitigate hazards via simulations and actual deployment scenarios, efficacy and dependability. this growing body of knowledge on how to protect lone workers in various workplaces by using the power of IoT and cloud computing. © 2024 ieee.

关键词： Cloud platforms

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Quantum Deep Q-Learning with distributed Prioritized Experience Replay 4

Quantum Deep Q-Learning with Distributed Prioritized Experie...

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4th ieee international conference on Quantum computing and Engineering, QCE 2023

作者： Chen, Samuel Yen-Chi Wells Fargo New YorkNY10017 United States

ISBN: (纸本)9798350343236

this paper introduces the QDQN-DPER framework to enhance the efficiency of quantum reinforcement learning (QRL) in solving sequential decision tasks. the framework incorporates prioritized experience replay, asynchronous training and novel matrix loss into the training algorithm to reduce the high sampling complexities. Numerical simulations demonstrate that QDQN-DPER outperforms the baseline distributed quantum Q-learning with the same model architecture. the proposed framework holds potential for more complex tasks while maintaining training efficiency. © 2023 ieee.

关键词： Reinforcement learning

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Green Virtual Networks for Timely Hybrid Synchrony distributed systems 4

Green Virtual Networks for Timely Hybrid Synchrony Distribut...

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4th international conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Hasan, Rasha Mendizabal, Odorico Dotti, Fernando Liwa College Faculty of Engineering and Computing Abu Dhabi United Arab Emirates Federal University of Santa Catarina Dept. of Informatics and Statistics Santa Catarina Brazil Pontifical Catholic University of RS Dept. of Compter Science Porto Alegre Brazil

ISBN: (纸本)9798350391183

Timely hybrid synchrony distributed systems are the kind of systems that do have timely-bounded synchrony constraints for both node processing execution and link message delivery. this hybrid synchrony feature proved to be useful for solving certain problems in the literature, for example, the consensus problem. A physical hybrid substrate for these systems is not a solution that goes in accordance with sus-Tainability standards for several reasons basically in terms of energy use, resource consolidation, E-resources waste, and data centers' environmental effect. In our work we propose virtual networks for data centers that host timely hybrid synchronous distributed systems, and this solution would result in minimizing the environmental impact, while still providing reliable and available solutions. this work is a continuity of our previous one where we proposed a resources allocation mathematical model. the contribution of our current work is threefold, firstly we motivate the use of hybrid synchrony virtual networks by clarifying sustainable aspects, along while listing real-life applications that would benefit from it, secondly, we detail the phases of the resources allocation process, and thirdy, we make further experiments that our previous ones with the objective of evaluating the performance of the micro-embedding phase. Results show that the proposed model can handle the embedding problem in an efficient manner within an acceptable solving time. © 2024 ieee.

关键词： Sustainability Synchrony Virtual Networks

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