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A Comprehensive Survey on Energy Efficiency in Federated Learning: Strategies and Challenges 8

A Comprehensive Survey on Energy Efficiency in Federated Lea...

引用

8th IEEE international Energy conference, ENERGYCON 2024

作者： Gouissem, Ala Chkirbene, Zina Hamila, Ridha College of Computing and Information Technology University of Doha for Science and Technology Qatar Electrical Engineering Qatar University Qatar

ISBN: (纸本)9798350382150

Federated Learning (FL), a burgeoning approach in machine learning, facilitates collaborative model training across distributed devices while maintaining data privacy. Although gaining traction, FL faces a critical challenge in energy efficiency, which is vital for its scalability and practicality, especially in resource-limited settings like IoT networks and mobile devices. this paper provides a comprehensive survey of current methods and techniques aimed at enhancing energy efficiency in FL systems. We delve into various resource allocation techniques and algorithm optimization strategies. Additionally, we examine the role of cutting-edge technologies such as Blockchain and 6G networks, which play a crucial role in minimizing the energy footprint of FL systems. Our survey pinpoints the principal challenges and identifies prospective areas for future research, intending to spur further advancements in energy-efficient FL. We discuss the intricate interplay between energy efficiency, model accuracy, and system scalability in FL. Furthermore, the paper emphasizes the real-world implications of these strategies, highlighting their practical relevance in various technological applications. ©2024 IEEE |

关键词： Energy efficiency

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Energy Efficient Dynamic Load Balancing using Self-Organized Criticality in Grid computing 24

Energy Efficient Dynamic Load Balancing using Self-Organized...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Kumar, Vivek Swain, Chinmaya Kumar Sahu, Aryabartta Iit Guwahati Dept. of Cse India Srm University Dept. of Cse India

ISBN: (纸本)9798350319934

Grid computing refers to a collection of compute resources from different geographic locations to achieve a common objective. It involves tremendous amounts of computational task which require reliable resource sharing across the grid for efficiently executing the tasks. Load disparity is a major issue in grid computing. So effectively and efficiently utilize the potentials of huge distributed resources, load balancing algorithms are of primary importance. In this paper, we proposed an elegant approach using Bak-Tang-Wiesenfeld sandpile model for dynamically load-balancing of tasks across the processing sites in an energy efficient manner. the proposed approach reduces redundant migrations between processing sites by transferring tasks in a breadth first manner. In this approach avalanche propagation condition is stricter and depends on the average load of two adjacent layers of neighbours and use of signalling mechanism rather than actual transfer of tasks during an avalanche, until a destination comes in equilibrium. We experimentally validated by a number of numerical experiments using Google traces. the results of the experiments show the effectiveness of the proposed approach which reduces the number of migrations and improves the energy efficiency without much increasing the total flowtime and degrading the load balancing aspects as compared to state-of-the-art approaches. © 2022 IEEE.

关键词： Energy efficiency

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Simplifying Graph-Parallel Computation in Apache Spark with GraphX 8

Simplifying Graph-Parallel Computation in Apache Spark with ...

引用

8th international conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Vyakaranam, Lasya Raman, Ramakrishnan Shah, Harshal Mishra, Nilamadhab Meenakshi, R. Murugan, S. Pune Maharashtra Pune India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Streamlining Apache graph-parallel computing the goal of integrating Spark with GraphX is to make processing massive amounts of graph data easier and faster. the goal is to streamline the process of creating and running graph algorithms by using GraphX, a Spark-based distributed graph processing platform. the eventual objective is to create a scalable platform that is easy to use for graph-structured data analysis and manipulation. Improving GraphX's performance, expanding its features, and integrating it with Spark's ecosystem are all part of this process. the expected result is a flexible and strong framework that lets data scientists and developers execute complicated graph analytics tasks with ease. this will pave the way for applications in many fields, including recommendation systems, social network analysis, and biological network analysis. Optimizing performance in graph-parallel computer situations, making the development process easier, and making it more scalable are the main goals. the graph structure visualization results in graph data revealing values in the range of 1.2 to 5.6 for a sample of five distinct vertex/edge types. the degree distribution histogram in the same dataset shows that, for five different frequencies, the numbers range from around 1.5 to 4.5. For the same dataset, the PageRank Convergence Plot shows values ranging from 0.4 to 0.9 for five distinct iterations/scores. © 2024 IEEE.

关键词： Apache Spark Big Data Graph Analytics Graph-Parallel Computation GraphX

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Aware: Adaptive distributed Training with Computation, Communication and Position Awareness for Deep Learning Model 24

Aware: Adaptive Distributed Training with Computation, Commu...

引用

作者： Zeng, Yan Yi, Guangzheng Yin, Yuyu Wu, Jiyang Xue, Meiting Zhang, Jilin Wan, Jian Zhang, Yunquan School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing China

ISBN: (纸本)9798350319934

the accuracy of the neural networks can usually be improved by increasing the size of the dataset and the layers or operators of the network, as it has strong composability. But, it makes a challenge to train these models efficiently, due to the limited resources of a single device, such as memory, computational resource and so on. For this challenge, it has become an inevitable trend to automatically implement model parallelism across multiple devices. this paper proposes Aware, an adaptive distributed parallel training method, to search distributed parallel strategy automatically for deep learning model. It firstly proposes an operator fusion strategy with computation and communication awareness to simplify the computational graph of deep learning model. And then, it introduces position-aware graph embedding algorithm to extract the structural features of models, which can make the searched parallel strategies to transplant to other networks with similar structures. On this basis, we use reinforcement learning algorithm to search distributed parallel strategy automatically. this paper makes experiments with neural networks such as Inception V3, NMT, GNM, NasNet and ResNet, and uses CIFAR10 and PTB datasets to compare the two methods of Hierarchical and Placeto. the results show that compared with Placeto, Aware achieves up to 5% reductions in runtime. Aware achieves more than 8% reductions in runtime compared with Hierarchical. Moreover, it has better transplantation and generalization capabilities, and supports pre-training for large-scale network parallel strategy search and accelerates convergence. © 2022 IEEE.

关键词： Deep learning

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Optimal Trajectory Planning and Task Assignment for UAV-assisted Fog computing 24

Optimal Trajectory Planning and Task Assignment for UAV-assi...

引用

作者： Liu, Shuaijun Yin, Jiaying Zeng, Zishu Wu, Jingjin BNU-HKBU United International College Department of Statistics and Data Science Zhuhai519087 China The University of Hong Kong Li Ka Shing Faculty of Medicine Pokfulam Hong Kong School of Engineering The Hong Kong University of Science and Technology Kowloon Hong Kong Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science China

ISBN: (纸本)9798350319934

Fog computing is an emerging distributed computing model for the realization of the Internet of things (IoT). It extends computing and caching functions to the edge of wireless networks. Unmanned Aerial Vehicles (UAVs) provide effective support for fog computing, as UAVs can not only act as a relay between mobile users and physically remote edge devices to avoid costly long-range wireless communications, but also are equipped with computing facilities that can take over certain tasks. In this paper, we aim to optimize the energy efficiency of a fog computing system assisted by a single UAV, by planning the trajectories of the UAV and assigning computing tasks to different devices including the UAV itself. We propose two algorithms based on the classical Ant Colony and Particle Swarm Optimization techniques, and solve the problem by continuous convex approximation. Different from most existing studies where the trajectories are assumed to be straight lines, we account for the effect of obstacles, such as buildings, and deliberately avoid them during the trajectory planning phase. We demonstrate, through extensive simulation experiments, that our proposed approach can achieve significantly better energy efficiency compared to existing benchmark algorithms. © 2022 IEEE.

关键词： Fog computing

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A Comparative Analysis on three Duplication Elements in Copy-Move Forgery using PatchMatch-Based Detection Method 8

A Comparative Analysis on Three Duplication Elements in Copy...

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8th IEEE international conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Nor Azaimi, Nur Izzati Abd Warif, Nor Bakiah Ismail, Nor-Syahidatul N Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Centre for Information Security Research Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Department of Computer System and Networking Pekan Pahang Malaysia

ISBN: (纸本)9798350310931

Image forgery is the alteration of a digital image to hide some of the important and useful information. Copy-move forgery (CMF) is one of the most difficult to detect because the copied part of the image has the same characteristics as the original image. Most of the existing datasets only highlight additional attacks in the copied part. Since there are no categories of duplication elements in the datasets, this research analyzed three categories of duplication elements in CMF which are animals, food and non-living things using DEFACTO and CoMo3Dataset. the analysis is performed on PatchMatch-based detection method and the results show that the method able to maintain at least 83% for all duplication elements in both DEFACTO and CoMo3Dataset. Furthermore, the method is able to detect a minimum 92% score for the food category in both datasets. © 2023 IEEE.

关键词： Copy-Move forgery DEFACTO duplication regions PatchMatch

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Supervised Multi-view Feature Selection Via Maximum Margin Criterion Joint distributed Optimization 8

Supervised Multi-view Feature Selection Via Maximum Margin C...

引用

8th IEEE international conference on Cloud computing and Intelligence Systems, CCIS 2022

作者： Lin, Qiang Jiangnan University School of Business Wuxi214122 China

ISBN: (纸本)9781665477352

this paper proposes a novel supervised multi-view feature selection method via maximum margin criterion (MMC) joint distributed optimization. Firstly, the proposed method integrates the common loss and the local loss of views to establish the global loss. Further, the view-based MMC regularizer and sparse regularizer are constructed, which can give the selected features better class separability. then the proposed method combines the distributed alternating direction method of multipliers (DADMM) to design a new algorithm to realize the block-based computation. Numerical experiments verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Feature Selection

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FedACL: Federated Multi-Distillation with Auxiliary Classification Layers in IoT 24

FedACL: Federated Multi-Distillation with Auxiliary Classifi...

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作者： Gao, Zhipeng Feng, Futeng Mo, Zijia Xiong, Zijian Yang, Yang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350319934

Federated learning is a popular privacy-preserving machine learning paradigm that can train a robust centralized model while guaranteeing the privacy of client data. However, traditional federated learning cannot cope with the problem of slow convergence and low accuracy due to model heterogeneity and statistical heterogeneity. To solve the problem, we propose a federated multi-distillation with auxiliary classification layers in IoT (FedACL). In FedACL, we add auxiliary classification layers after certain hidden layers in the local heterogeneous model, thus implementing a multi-distillation framework consisting of self-distillation in the local update and co-distillation in the communication. Besides, in order to improve the stability of model training, we propose a double-weighted method based on the test accuracy of the clients to measure the contribution of each client. A large number of experiments are conducted for different numbers of client and communication volumes under the Non-IID settings of CIFAR100 and FEMNIST datasets, and all of them prove that FedACL has fast convergence speed, high accuracy and good stability. © 2022 IEEE.

关键词： Distillation

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Task Scheduling for ICN-Based computing First Network: A Deep Reinforcement Learning Approach 8

Task Scheduling for ICN-Based Computing First Network: A Dee...

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8th IEEE international conference on Computer and Communications, ICCC 2022

作者： Zou, Zhuang Xie, Renchao Ren, Yuzheng Yu, F. Richard Huang, Tao Beijing University of Posts and Telecommunications State Key Lab. of Networking and Switching Technology Beijing China Carleton University Depart. of Systems and Computer Eng. OttawaON Canada

ISBN: (纸本)9781665450515

the computing first network (CFN) combines heterogeneous computing force information with network information and improves resource utilization and task execution efficiency through resource perception, service positioning, and task scheduling. However, since the heterogeneous computing force is difficult to express and perceive, and it is distributed on each node of the edge network, it is difficult to schedule tasks uniformly. therefore, this paper proposes a CFN architecture based on information-centric network (ICN), which uses the naming mechanism of ICN to characterize the computing force and tasks, and the caching mechanism and routing and forwarding mechanism to improve the computing efficiency in the network. Under this network architecture, we study the scheduling problem of multi-user tasks in a period, model it as a multi-objective optimization model, and transform it into a Markov decision process (MDP) to enable control nodes to prioritize tasks and then schedule them to computing nodes for execution, which takes into account the task queue status and node resources. To solve the proposed problem, we use the deep reinforcement learning algorithm to approximate the optimal task scheduling solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme. © 2022 IEEE.

关键词： Reinforcement learning

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BYEE: Batch Fully Private Scheme for Coded distributed Matrix Multiplication 24

BYEE: Batch Fully Private Scheme for Coded Distributed Matri...

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作者： Li, Guojing Wang, Jin Zou, Xiaotian Li, Lingzhi Gu, Fei School of Computer Science and Technology Soochow University Suzhou215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization China

ISBN: (纸本)9798350319934

Recently, user privacy in distributed computing has received increasing attention. Matrix multiplication is one of the fundamental high-frequency operations in distributed machine learning (e.g., gradient descent, linear regression). this paper studies the batch Fully Private distributed Matrix Multiplication (FPMM) problem. In batch FPMM, the user aims to compute multiple desired matrix multiplications from two public libraries which are stored across all workers. Batch FPMM requires that colluding workers cannot obtain the indexes of all desired ma-trices in the two public libraries (i.e., privacy constraint). In this paper, we first propose the Bath fullY privatE schemE (BYEE) to encode multiple desired matrix multiplications together based on the construction of bilinear complexity. We then perform a solid theoretical analysis to prove the privacy and decodability of BYEE. Multi-aspect analysis and extensive simulation results illustrate that BYEE can significantly reduce the computation latency than state-of-the-art schemes that do not jointly encode multiple desired matrix multiplications, especially when the number of desired matrix multiplications and the number of colluding workers are large. © 2022 IEEE.

关键词： Libraries

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