An efficient algorithm for energy harvesting in IIoT based on machine learning and swarm intelligence

Xing, Peizhen; Zhang, Hui; Derbali, Morched; Sefat, Shebnam M.; Alhussan, Amel Ali; Khafaga, Doaa Sami; Sani, Nor Samsiah

doi:10.1016/j.heliyon.2023.e17622

Heliyon

2023

DOI: 10.1016/j.heliyon.2023.e17622

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An efficient algorithm for energy harvesting in IIoT based on machine learning and swarm intelligence

Peizhen Xing¹,

Hui Zhang²,

Morched Derbali³

et al.

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2024

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A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing

Marin Machado de Souza,

Holm,

Biczyk

et al. 2024

Electronics

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Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary fields, have gained significant attention in the machine learning community due to their unique characteristics. FL enables decentralized machine learning by allowing models to be trained on data residing across multiple devices or servers without exchanging raw data, thus enhancing privacy and reducing communication overhead. Conversely, BIC draws inspiration from nature to develop robust and adaptive computational solutions for complex problems. This paper explores the state of the art in the integration of FL and BIC, introducing BIC techniques and discussing the motivations for their integration with FL. The convergence of these fields can lead to improved model accuracy, enhanced privacy, energy efficiency, and reduced communication overhead. This synergy addresses inherent challenges in FL, such as data heterogeneity and limited computational resources, and opens up new avenues for developing more efficient and autonomous learning systems. The integration of FL and BIC holds promise for various application domains, including healthcare, finance, and smart cities, where privacy-preserving and efficient computation is paramount. This survey provides a systematic review of the current research landscape, identifies key challenges and opportunities, and suggests future directions for the successful integration of FL and BIC.

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A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing

Marin Machado de Souza,

Holm,

Biczyk

et al. 2024

Electronics

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show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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An efficient algorithm for energy harvesting in IIoT based on machine learning and swarm intelligence

Cited by 1 publication

References 33 publications

A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing

A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing

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