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Evaluation and optimization of carbon emission for federal edge intelligence network
Published 2024-03-01“…In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.…”
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Novel Bounds for Incremental Hessian Estimation With Application to Zeroth-Order Federated Learning
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Evaluation of levels of exposure of adult patients from common radiographic examinations in the Russian Federation in 2009–2014
Published 2017-10-01“…Results and discussion: Results of the dose data analysis indicate the lack of significant differences between the distributions of the typical effective doses between the selected regions, allowing merging the regional samples and further evaluating the pooled (joint) sample. …”
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Statistical evaluation of the quality of working life of employees in the russian federation
Published 2018-09-01“…The analysis that is based on data from Federal State Statistics Service has been done in view of gender factors and federal district differences. …”
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Federated and ensemble learning framework with optimized feature selection for heart disease detection
Published 2025-03-01“…Furthermore, we used four performance metrics, namely accuracy, precision, recall, and F1score, to evaluate the selected models. Finally, federated learning was included to handle privacy issues and guarantee safe access to private medical data. …”
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Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
Published 2025-02-01“…To understand the influence of data heterogeneity, a self-evaluating federated learning framework for heterogeneity, <b>Fed-Hetero,</b> was designed to assess the type of heterogeneity associated with the clients and provide recommendations to clients to enhance the global model’s accuracy. …”
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Hybrid quantum enhanced federated learning for cyber attack detection
Published 2024-12-01“…Due to this, conventional methods are unable to adapt quickly for different threats. This research aims to develop a novel solution to address these limitations through Federated Learning. …”
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Bio-Inspired Hyperparameter Tuning of Federated Learning for Student Activity Recognition in Online Exam Environment
Published 2024-07-01“…It achieves this by accurately classifying students’ attentiveness based on distinct head poses, a novel approach that leverages advanced techniques like federated learning and deep learning models. The proposed work aims to classify students’ attentiveness with the help of different head poses. …”
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Heuristic based federated learning with adaptive hyperparameter tuning for households energy prediction
Published 2025-04-01“…However, the prediction accuracy of federated learning models tends to diminish when dealing with non-IID data highlighting the need for adaptive hyperparameter optimization strategies to improve performance. …”
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Enhancing MANET Security Through Federated Learning and Multiobjective Optimization: A Trust-Aware Routing Framework
Published 2024-01-01“…We evaluate this framework against existing protocols across various scenarios, including different network densities, mobility patterns, and malicious node percentages. …”
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Performance of an End-to-End Inventory Demand Forecasting Pipeline Using a Federated Data Ecosystem
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Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security
Published 2025-06-01“…This involves the incorporation of the Federated Residual Convolutional Network into an optimization method called EEL-Levy Fusion Optimization. …”
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A novel diagnosis methodology of gear oil for wind turbine combining Stepwise multivariate regression and clustered federated learning framework
Published 2025-07-01“…To enhance diagnostic performance, we introduce a clustered federated learning framework (CFLF) for wind gear oil diagnosis. …”
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Comparative Evaluation of Different Fuzzy Tuning Rules on Energy Management Systems Cost Savings
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Planning and Evaluation of Bioequivalence Studies of Telmisartan Generic Drug Products
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