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eFFICIeNT FeDeRATeD LeARNING WITH SMOOTH AGGReGATION FOR NON-IID DATA FROM MULTIPLe eDGeS 49

EFFICIENT FEDERATED LEARNING WITH SMOOTH AGGREGATION FOR NON...

引用

49th ieee international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wang, Qianru Li, Qingyang Guo, Bin Cui, Jiangtao Xidian Univ Xian Shaanxi Peoples R China Northwestern Polytech Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Federated learning (FL) learns an optimal global model by aggregating local models trained on distributed data from different devices. Due to heterogeneous data distributions across devices, local models will be divergent, resulting in the global model's performance degradation. Recent studies attempt to balance local models to obtain a global model that can adapt to each device. But they ignore a more challenging problem that redundant local models from devices will break the balance, resulting in the global model overfitting redundant local models. Therefore, we propose FedSmooth, a novel global aggregation algorithm. FedSmooth first identifies the redundant local models without sensitive local information (e.g., label distribution), then designs a smooth global aggregation to strengthen the effect of local models that can accelerate finding the optimal global model. experimental results show that our method outperforms 4 SOTA baseline methods even if there is more redundancy.

关键词： Federated learning edge-collaborative computing redundant data deep neural network

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grid Tied Photovoltaic Based electric Vehicle Charging Infrastructure 2

Grid Tied Photovoltaic Based Electric Vehicle Charging Infra...

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2nd ieee international conference on Advances in electrical, computing, Communication and Sustainable Technologies (ieee ICAeCT)

作者： Gupta, Mamta Seth, Aakash Kumar Singh, Mukhtiar Delhi Technol Univ Dept Elect Engn Delhi India

ISBN: (纸本)9781665411202

This paper presents detailed simulation of grid tied photovoltaic ( PV) based an electric vehicle (eV) charging infrastructure. The eV charger can work in both modes of power flow i.e., grid to vehicle (G2V) and vehicle to grid (V2G), which can be employed in diverse load power requirement scenarios. The overall system uses various converters at each stage, boost converter for harnessing solar PV power, a bidirectional buck-boost converter for eV charging, and a voltage source converter (VSC) with LCL filter for integrating overall system with the grid. The controlling of DC-DC boost converter for extracting peak power output is done using maximum power point tracking (MPPT) technique. The controller associated with bidirectional buck-boost converter is used for controlling power flow of battery pack. Moreover, the controller of grid side bidirectional AC-DC converter is utilized for controlling DClink voltage. The system presented in this paper contributes towards maximizing energy output from PV system with perturbation and observation (P&O), uninterrupted eV charging, enhancing grid performance and using eV as a form of energy storage system when required. extensive simulation for various power flow modes have been performed to verify the aforementioned claims. The overall design and simulation work is executed in MATLAB/Simulink software and results are confirmed for various input conditions.

关键词： Photovoltaic Vehicle to grid grid to Vehicle eV charging grid Power Support

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Analyzing the Impact of Quantum Cryptography on Network Security 2

Analyzing the Impact of Quantum Cryptography on Network Secu...

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2nd ieee international conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth e Polimetla, Kiran University of North Dakota School of Electrical Engineering and Computer Science Grand ForksND United States Adobe Inc Dept. of Information Technology AustinTX United States

ISBN: (纸本)9798350317558

Quantum cryptography is a field that employs principles of quantum physics to communicate via networks. It has developed as an ability to handle the growing risks to network security, including cyber-attacks and data breaches. This age offers superior and advanced security characteristics compared to traditional cryptographic systems, making it a highly promising choice for safeguarding sensitive data. Quantum cryptography provides secure communication channels that are resistant to eavesdropping and hacking. Quantum key distribution (QKD) algorithms are utilized to generate and distribute encryption keys by using the distinct properties of quantum particles. Intercepting the keys without altering their quantum state is exceedingly difficult, requiring precise manipulation. Quantum cryptography has a notable effect on community defense by effectively mitigating the threat posed by quantum computing. Due to the rise of quantum computing, conventional encryption methods are becoming more susceptible to attacks that could compromise the security of encryption keys. © 2024 ieee.

关键词： Communication computing Cryptography Protocols Quantum

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Minimizing Hybrid electric Power Generation Costs Using Smart Power grid Solution 23

Minimizing Hybrid Electric Power Generation Costs Using Smar...

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23rd ieee international conference on Machine Learning and Applications, ICMLA 2024

作者： Mehta, Vijen Agrawal, Kunal Madabathula, Charan Teja Wang, Ming-Hwa San Jose State University Dept. Applied Data Science San Jose United States

ISBN: (纸本)9798350374889

Increasing global electricity demand, coupled with resource constraints, necessitates strategic generation planning to optimize costs and curb emissions. This study leverages California's hourly electric generation data (renewable and non-renewable), demand consumption data, and cost of generation to develop Machine Learning models for forecasting electricity demand, resulting in mitigating costs and emissions. Moreover, we propose an innovative grid system, including trigger mechanisms, to redirect forecasted surplus electricity, back into methods of electric generation, minimizing operating costs while satisfying demand. The Random Forest Regressor model outperformed other models used to forecast electric demand. It achieved a Mean Absolute Percentage error (MAPe) of 2.9%, Root Mean Squared error (RMSe) of 1087.9, and R-squared (R2) of 95.5%. Using the forecasted demand data and leveraging our hybrid energy grid system, we applied a mathematical algorithm that resulted in significant cost savings and emission reductions during 8-hours of electricity overproduction in California during summer. By strategically redirecting excess electricity back into methods of generation, it facilitates savings of up to $525,596.68 in a single day and avoids emissions equivalent to 5252.38 tCO2e compared to traditional grid methods. The results achieved through our proposed methods offer a promising approach for optimizing generation costs and promoting sustainable electricity production. © 2024 ieee.

关键词： Demand response

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Music Recommender System using Non-Negative Matrix Factorization

Music Recommender System using Non-Negative Matrix Factoriza...

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2025 international conference on Advancement in Data science, e-learning and Information System, ICADeIS 2025

作者： Athari, Syafreza Hanif Baizal, Z.K.A. School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Recommender systems play a crucial role in music streaming services like Spotify to help users discover music that aligns with their preferences. The goal of this research is to create a music recommendation system that utilizes collaborative filtering with Non-Negative Matrix Factorization (NMF). Matrix Factorization decomposes user-music interactions into smaller matrices to predict user preferences for songs they have not listened to yet. This method will be compared with SVD to see if NMF is better at recommending music that match their likes and preferences. NMF imposes a non-negativity constraint on the decomposed matrices, making the resulting factors easier to interpret. Performance evaluation is conducted using RMSe (Root Mean Squared error) and MAe (Mean Absolute error) metrics to find out how good the method provides recommendations. Based on the test results, the NMF method with an RMSe value of 0.4727 and MAe value 0.3693, indicating its effectiveness in providing music recommendations. © 2025 ieee.

关键词： Non-negative matrix factorization

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Improving the efficiency of Smart grid Scheduling: A Linear Regression Model with Meta Job Information 2

Improving the Efficiency of Smart Grid Scheduling: A Linear ...

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2nd international conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia College Of Information Technology University Of Petra Department Of Computer Science Amman Jordan German Jordianian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

In order to forecast the run time of the jobs that were submitted, this research provides two linear regression prediction models that include continuous and categorical factors. A continuous predictor is built using the number of CPUs, average CPU speed, and memory size, whereas a categorical predictor is built using the user ID, group ID, and executable ID. According to the findings, the prediction rates for categorical and continuous predictors are 61% and 1%, respectively. sixty times better than the earlier models, which used continuous variables as a foundational model to determine task complexity and weight. The effectiveness of the categorical predictor in enhancing a job scheduling problem is then evaluated by combining it with three suggested job scheduling strategies. The suggested algorithms incorporated metrics - predicted run time, waiting time, and resource requirement - to select the smallest jobs. According to the results, Algorithm 3 performs better than earlier models in both performance metrics. The variation in total execution time and average waiting time is 1.14 to 1.76 and 4.5, respectively compared to previous models, Additionally, Algorithms 1 and 2 demonstrating superior performance in every scenario. © 2024 ieee.

关键词： grid computing

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Optimal Allocation Strategy of Frequency Modulation Power for Multiple energy Storage Systems Considering Dual Constraints of Frequency Modulation Cost and Frequency Modulation Capability

Optimal Allocation Strategy of Frequency Modulation Power fo...

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2023 ieee international conference on Power science and Technology, ICPST 2023

作者： Peng, Yongming Si, Wenbo Zhao, Haiji Lv, Changlin Liu, Chengzhe Xu, Fei Hao, Ling Li, Junhui Northeast Branch of State Grid Corporation of China Shenyang China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Jilin China State Key Laboratory of Power System and Power Generation Equipment Control and Simulation Beijing China

ISBN: (纸本)9798350311358

Aiming at the power allocation problem of multiple energy storage power stations distributed at different locations in the regional power grid participating in frequency modulation services, a frequency modulation power optimization allocation strategy for multiple energy storage systems considering the dual constraints of frequency modulation cost and frequency modulation capability is proposed. Firstly, a multi-objective optimization allocation model for frequency modulation power is constructed with the minimum frequency modulation cost and minimum deviation of the state of charge (SOC) for energy storage power plants. Secondly, using Pareto based multi-objective genetic algorithm (MOGA) to solve the model, introduce adaptive weight coefficients and fuzzy membership functions for each objective function regarding the frequency regulation demand of the power grid and the deviation of the SOC of energy storage stations, construct a comprehensive satisfaction function to make decisions on the most advantageous points on the Pareto frontier, in order to obtain the optimal output of each energy storage station that can adapt to the frequency regulation requirements of the power grid. Finally, simulation analysis was conducted on the proposed strategy in this article, and the results showed that: The proposed strategy not only enhances the frequency regulation economy of the regional power grid, but also helps to restore the SOC of the energy storage power station. © 2023 ieee.

关键词： Frequency modulation

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The Smart Optimization of Scalable Probabilistic Modeling with Gaussian Processes

The Smart Optimization of Scalable Probabilistic Modeling wi...

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2024 international conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Gobi, N. Awasthi, Aishwary Dwivedi, Rakesh Kumar Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

The intelligent Optimization of Scalable Probabilistic Modeling with Gaussian processes (SPMGP) is a sophisticated and speedy method for probabilistic modeling and scalable computation. It offers a framework to effectively and successfully reduce the overhead associated with optimizing a probabilistic model while exploiting the predictive talents of Gaussian techniques. SPMGP successfully reduces the computational cost required to optimize large-scale fashions past the conventional optimization strategies. It compensates for the low accuracy of grid search-primarily based optimization procedures in favorable situations. Moreover, SPMGP permits green and robust hyperparameter tuning for a wide variety of gaining knowledge of obligations. SPMGP is predicated on exploiting the advanced predictive overall performance of the Gaussian process within the optimization method by way of estimating the most desirable fee with fewer samples than traditional techniques while presenting good generalization overall performance. Additionally, SPMGP uses a heuristic to prune the hunt area for a given trouble, further reducing the computational fee of hyperparameter tuning. It allows for a greater green and scalable model optimization with fewer hyperparameter. Moreover, SPMGP offers a framework to use exceptional getting-to-know practices to enhance the version metrics without growing the value of the version. © 2024 ieee.

关键词： compensates hyperparameter Optimization probabilistic scalable

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ethereum Fraud Detection: A comparative analysis of supervised learning approach 1

Ethereum Fraud Detection: A comparative analysis of supervis...

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1st ieee international conference on Advances in Signal Processing, Power, Communication, and computing, ASPCC 2024

作者： Tripathy, Nrusingha Chaudhury, Kumar Surjeet Nibedita, Arpita Nayak, Subrat Kumar Behera, Manjushree Jena, Baidehi Siksha O Anusandhan Deemed to Be University Department of Computer Science and Engineering Odisha Bhubaneswar India Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India Trident Academy of Technology School of Computing Odisha Bhubaneswar India P.N College Department of Computer Science Khordha India

ISBN: (纸本)9798350355093

ethereum is an application platform that distributes versions of intelligent contracts to thousands of people globally, utilizing blockchain to decentralize data. ethereum is a global currency that is used to exchange value without requiring supervision or outside involvement. However, as e-commerce grows, the biggest threat to trade security is the proliferation of illegal activities like phishing, money laundering, and bribery. The need for strong defenses is highlighted by the inherent hazards of blockchain technology, such as the potential for fraud and cyberattacks. The stability and preservation of confidence in the financial system depend on an accessible network that is impervious to these kinds of assaults. Different classification algorithms, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and extreme Gradient Boosting (XGBoost) are being used in this work to detect ethereum fraud. We are utilizing a dataset that includes rows of ethereum cryptocurrency transactions along with rows of known unauthorized transactions. Notably, the 'XGBoost' model identifies differences that might draw attention and avert possible problems in this task. Supervised learning for ethereum fraud detection improves the ecosystem's security and integrity. © 2024 ieee.

关键词： ethereum

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Focusing on topology map generation and adjustment in power network asset mapping scenarios 8

Focusing on topology map generation and adjustment in power ...

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8th international conference on electrical, Mechanical and Computer engineering, ICeMCe 2024

作者： Zhu, Hongyu Luo, Weiqiang Tian, Zheng Tao, Kai Chen, Qian Xu, Fangming State Grid Hunan Electric Power Company Limited Information and Communication Company Changsha China Hunan Key Laboratory for Internet of Things in Electricity Changsha China State Grid Hunan Electric Power Co. Ltd Changsha China Hunan University College of Computer Science and Electronic Engineering Changsha China

ISBN: (纸本)9798331506230

Power system network topology mapping technology performs path analysis and calculation of devices, generates topology information and draws network asset topology maps, which helps to realize the visualization management of power network assets. However, the traditional network topology generation method has the problem of redundant calculation of paths of all nodes due to changes in some devices, which increases the loss of computing resources. In addition, the network topology graph generated by the traditional method is not specifically planned for the specific layout of the asset mapping scenario, which is not in line with the usage habits of the relevant operation and maintenance personnel. For this reason, this paper proposes a power network topology generation and layout adaptive adjustment algorithm, which realizes the generation and adjustment of network topology globally or locally. At the same time, the network topology layout for new power system asset mapping scenarios is generated. experiments show that compared with traditional topology mapping techniques, the method can generate topology maps that combine the characteristics of asset mapping scenarios, and can adjust the local topology according to the changes in the access assets in a targeted manner to improve the efficiency of network asset detection. © 2024 ieee.

关键词： Network topology

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