Agricultural data privacy and federated learning: A review of challenges and opportunities

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110048 - 110048

Published: Feb. 10, 2025

Language: Английский

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Multi-antenna Arrays Based Massive-MIMO for B5G/6G: State-of-the-Art, Challenges and Future Research Directions

Published: June 4, 2024

This comprehensive article explores Massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, Cell-Free MIMO, security considerations, delves into intricacies of M-MIMO in evolving landscape B5G. Moreover, emerging concepts this include AI-enabled three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, THz spectrum utilization. review highlights challenges open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Arrays, Integrated Sensing with M-MIMO, application Federated Learning systems. By examining these cutting-edge developments, aims to contribute advancing knowledge field inspire future directions exciting realm B5G

Language: Английский

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Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2 T and adaptive boosted decision fusion with babysitting refinement

Frontiers in Medicine, Journal Year: 2024, Volume and Issue: 11

Published: July 5, 2024

Global Cardiovascular disease (CVD) is still one of the leading causes death and requires enhancement diagnostic methods for effective detection early signs prediction outcomes. The current tools are cumbersome imprecise especially with complex diseases, thus emphasizing incorporation new machine learning applications in differential diagnosis.

Language: Английский

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FLSecure: A hybrid framework with blockchain and multi-TEE parallel execution for secure federated learnings

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 122, P. 300 - 317

Published: March 15, 2025

Language: Английский

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Efficient clustered federated learning for Industrial Internet of Things: enhancing predictive performance and training time

Deleted Journal, Journal Year: 2025, Volume and Issue: 28(1)

Published: April 15, 2025

Abstract The Industrial Internet of Things (IIoT) brings together industrial devices in a network that gathers and analyzes data real-time for making data-driven decisions. Federated learning is popular approach collaboratively training multiple edge using an intermediate server rounds. This can be applied various fields, including anomaly detection, asset management, energy efficiency, quality control, predictive maintenance. However, performance affected by limited non-independent, identically distributed (non-IID) data. Additionally, also face resource constraints large datasets. paper proposes cluster-assisted custom federated improving the prediction resources required training. initializes model broadcasting initial parameters, then start After on current round’s data, transmit updated performance, distribution back to server. Then, clusters based their minimize non-IID. Parameter aggregation undertaken within cluster improve aggregated parameter sent respective members. Assuming secure internal network, work share samples round increase dataset size diversity. Earlier portion datasets are excluded from reduce drift. Comprehensive experimental evaluation with testbed proves effectiveness proposed over state-of-the-art.

Language: Английский

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Advanced Optimization Techniques for Federated Learning on Non-IID Data

Future Internet, Journal Year: 2024, Volume and Issue: 16(10), P. 370 - 370

Published: Oct. 13, 2024

Federated learning enables model training on multiple clients locally, without the need to transfer their data a central server, thus ensuring privacy. In this paper, we investigate impact of Non-Independent and Identically Distributed (non-IID) performance federated training, where find reduction in accuracy up 29% for neural networks trained environments with skewed non-IID data. Two optimization strategies are presented address issue. The first strategy focuses applying cyclical rate determine during while second develops sharing pre-training method augmented order improve efficiency algorithm case By combining these two methods, experiments show that CIFAR-10 dataset increased by about 36% achieving faster convergence reducing number required communication rounds 5.33 times. proposed techniques lead improved convergence, representing significant advance field facilitating its application real-world scenarios.

Language: Английский

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Multi-Antenna Array-Based Massive MIMO for B5G/6G: State of the Art, Challenges, and Future Research Directions

Information, Journal Year: 2024, Volume and Issue: 15(8), P. 442 - 442

Published: July 29, 2024

This comprehensive article explores the massive MIMO (M-MIMO) design and its associated concepts, focusing on seamless integration requirements for Beyond 5G (B5G) 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, cell-free MIMO, security considerations, this delves into intricacies of M-MIMO in evolving landscape B5G. Moreover, emerging concepts include AI-enabled three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, THz spectrum utilization. review highlights challenges open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Arrays, Integrated Sensing with M-MIMO, application federated learning systems. By examining these cutting-edge developments, aims to advance knowledge field inspire future directions exciting realm B5G

Language: Английский

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Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(7), P. 2720 - 2720

Published: March 24, 2024

Federated learning has emerged as a promising approach for collaborative model training across distributed devices. faces challenges such Non-Independent and Identically Distributed (non-IID) data communication challenges. This study aims to provide in-depth knowledge in the federated environment by identifying most used techniques overcoming non-IID that communication-efficient solutions learning. The highlights types, models, datasets A systematic mapping was performed using six digital libraries, 193 studies were identified analyzed after inclusion exclusion criteria applied. We enhancing aggregation method clustering are widely problems (used 18% 16% of selected studies), quantization technique common 27% 15% studies). Additionally, our work shows label distribution skew is case simulate environment, specifically, quantity imbalance. supervised CNN commonly model, image MNIST Cifar-10 when evaluating proposed approaches. Furthermore, we believe research community needs consider client’s limited resources importance their updates addressing prevent loss valuable unique information. outcome this will benefit users, researchers, providers.

Language: Английский

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Design and implementation of privacy-preserving federated learning algorithm for consumer IoT

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 106, P. 206 - 216

Published: July 10, 2024

Language: Английский

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FedWFC: Federated learning with weighted fuzzy clustering for handling heterogeneous data in MIoT networks

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 111, P. 194 - 202

Published: Oct. 22, 2024

Language: Английский

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