Evaluation and optimization of carbon emission for federal edge intelligence network by Peng ZHANG, Yong XIAO, Jiwei HU, Liang LIAO, Jianxin LYU, Zegang BAI

“…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 by Alessio Maritan, Luca Schenato, Subhrakanti Dey

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Evaluation of levels of exposure of adult patients from common radiographic examinations in the Russian Federation in 2009–2014 by A. V. Vodovatov, V. Yu. Golikov, S. A. Kalnitsky, I. G. Shatsky, L. A. Chipiga

“…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 by Vera V. Zholudeva, Nadezhda F. Melnichenko

“…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 by Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui

“…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 by Aiswariya Milan Kummaya, Amudha Joseph, Kumar Rajamani, George Ghinea

“…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 by G. Subramanian, M. Chinnadurai

“…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 by Ramu Shankarappa, Nandini Prasad, Ram Mohana Reddy Guddeti, Biju R. Mohan

“…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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FedDyH: A Multi-Policy with GA Optimization Framework for Dynamic Heterogeneous Federated Learning by Xuhua Zhao, Yongming Zheng, Jiaxiang Wan, Yehong Li, Donglin Zhu, Zhenyu Xu, Huijuan Lu

“…Prior to this work, few studies have explored the use of optimization algorithms for hyperparameter tuning in federated learning. …”
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Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection by Amogh Deshmukh, Peplluis Esteva de la Rosa, Raul Villamarin Rodriguez, Sandeep Dasari

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Heuristic based federated learning with adaptive hyperparameter tuning for households energy prediction by Liana Toderean, Mihai Daian, Tudor Cioara, Ionut Anghel, Vasilis Michalakopoulos, Efstathios Sarantinopoulos, Elissaios Sarmas

“…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 by Saad M. Hassan, Mohd Murtadha Mohamad, Farkhana Binti Muchtar, Firoz Bin Yusuf Patel Dawoodi

“…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 by Henrique Duarte Moura, Els de Vleeschauwer, Gerald Haesendonck, Ben De Meester, Lynn D’eer, Tom De Schepper, Siegfried Mercelis, Erik Mannens

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Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security by Nalini Manogaran, Yamini Bhavani Shankar, Malarvizhi Nandagopal, Hui-Kai Su, Wen-Kai Kuo, Sanmugasundaram Ravichandran, Koteeswaran Seerangan

“…This involves the incorporation of the Federated Residual Convolutional Network into an optimization method called EEL-Levy Fusion Optimization. …”
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Evaluation of Emulsification Techniques to Optimize the Properties of Chalcone Nanoemulsions for Antifungal Applications by Joice Farias do Nascimento, Flavia Oliveira Monteiro da Silva Abreu, Taysse Holanda, Raquel Oliveira dos Santos Fontenelle, Júlio César Sousa Prado, Emmanuel Silva Marinho, Matheus Nunes da Rocha, Jesyka Macêdo Guedes, Bruno Coelho Cavalcanti, Wesley Lyeverton Correia Ribeiro, Márcia Machado Marinho, Helcio Silva dos Santos

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A novel diagnosis methodology of gear oil for wind turbine combining Stepwise multivariate regression and clustered federated learning framework by Huihui Han, Ye Zhao, Hao Jiang, Muxin Chen, Song Zhou, Zihan Lin, Xin Wang, Boman Mao, Xinyue Yang, Yuchun Li

“…To enhance diagnostic performance, we introduce a clustered federated learning framework (CFLF) for wind gear oil diagnosis. …”
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Prospects for the reduction of the patient doses based on the optimization of the CT abdomen protocols for the different types of malignancies by A. N. Bashkov, Zh. V. Sheykh, S. E. Voskanyan, A. P. Dunaev, M. V. Popov, Yu. D. Udalov, A. S. Samoylov

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Comparative Evaluation of Different Fuzzy Tuning Rules on Energy Management Systems Cost Savings by Oladimeji Ibrahim, Mutiu Shola Bakare, Waheed Olaide Owonikoko, Rasaq Atanda Alao, Temitope Ibrahim Amosa, Muhyideen Oluwafemi Tijani

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Economically optimal water depth and grain yield of common bean subjected to different irrigation depths by Chaiane G. da Conceição, Adroaldo D. Robaina, Marcia X. Peiter, Ana R. C. Parizi, João A. da Conceição, Jhosefe Bruning

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Planning and Evaluation of Bioequivalence Studies of Telmisartan Generic Drug Products by A. L. Khokhlov, D. P. Romodanovsky, A. E. Miroshnikov

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