Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration
The study explores resource allocation in Federated Machine Learning (FedML) for the Industrial Internet of Things (IIoT), focusing on efficient and privacy-conscious data processing. It proposes optimizing the FedML training process to enhance system performance and ensure data privacy. The researc...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025024570 |
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| author | Ala'a R. Al-Shamasneh Faten Khalid Karim Yu Wang |
| author_facet | Ala'a R. Al-Shamasneh Faten Khalid Karim Yu Wang |
| author_sort | Ala'a R. Al-Shamasneh |
| collection | DOAJ |
| description | The study explores resource allocation in Federated Machine Learning (FedML) for the Industrial Internet of Things (IIoT), focusing on efficient and privacy-conscious data processing. It proposes optimizing the FedML training process to enhance system performance and ensure data privacy. The research also presents a long-term average unified cost minimization problem considering energy constraints, device heterogeneity, and limited bandwidth in federated edge learning contexts. To address these issues, a novel Lyapunov-driven optimization algorithm for device selection and bandwidth allocation is introduced. This algorithm adeptly balances resource expenditures with model quality, employing Lyapunov-driven optimization theory to convert long-term stochastic challenges into short-term deterministic resolutions. Furthermore, the study presents a multi-tier federated edge learning architecture that integrates cloud collaboration with edge servers to manage the increasing number of industrial devices and the demand for timely local model training. Simulations confirm the methods' low complexity and superior efficacy, highlighting reduced system delay and enhanced model accuracy. The proposed method reduced system delay by up to 30%, achieved a model accuracy of 98% on the MNIST dataset and 91% on CIFAR-10, and improved convergence speed, with training loss decreasing by 25% within the first 10 rounds. The method also achieved a 40.5% improvement in computational efficiency and a 30-50% reduction in system costs, demonstrating its practicality and scalability. These results augment the performance of federated machine learning applications in practical IIoT settings. The insights garnered facilitate the development of intelligent industrial systems that prioritize efficiency and data privacy. |
| format | Article |
| id | doaj-art-bc66ed66bba04471aedba30ee5913d3f |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-bc66ed66bba04471aedba30ee5913d3f2025-08-20T03:32:58ZengElsevierResults in Engineering2590-12302025-09-012710638710.1016/j.rineng.2025.106387Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integrationAla'a R. Al-Shamasneh0Faten Khalid Karim1Yu Wang2Department of Computer Science, College of Computer & Information Sciences, Prince Sultan University, Rafha Street, Riyadh 11586, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; Corresponding author.Shandong Research Institute of Industrial Technology, Jinan, 250000, ChinaThe study explores resource allocation in Federated Machine Learning (FedML) for the Industrial Internet of Things (IIoT), focusing on efficient and privacy-conscious data processing. It proposes optimizing the FedML training process to enhance system performance and ensure data privacy. The research also presents a long-term average unified cost minimization problem considering energy constraints, device heterogeneity, and limited bandwidth in federated edge learning contexts. To address these issues, a novel Lyapunov-driven optimization algorithm for device selection and bandwidth allocation is introduced. This algorithm adeptly balances resource expenditures with model quality, employing Lyapunov-driven optimization theory to convert long-term stochastic challenges into short-term deterministic resolutions. Furthermore, the study presents a multi-tier federated edge learning architecture that integrates cloud collaboration with edge servers to manage the increasing number of industrial devices and the demand for timely local model training. Simulations confirm the methods' low complexity and superior efficacy, highlighting reduced system delay and enhanced model accuracy. The proposed method reduced system delay by up to 30%, achieved a model accuracy of 98% on the MNIST dataset and 91% on CIFAR-10, and improved convergence speed, with training loss decreasing by 25% within the first 10 rounds. The method also achieved a 40.5% improvement in computational efficiency and a 30-50% reduction in system costs, demonstrating its practicality and scalability. These results augment the performance of federated machine learning applications in practical IIoT settings. The insights garnered facilitate the development of intelligent industrial systems that prioritize efficiency and data privacy.http://www.sciencedirect.com/science/article/pii/S2590123025024570Industrial Internet of ThingsFederated learningResource allocationMobile edge computing |
| spellingShingle | Ala'a R. Al-Shamasneh Faten Khalid Karim Yu Wang Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration Results in Engineering Industrial Internet of Things Federated learning Resource allocation Mobile edge computing |
| title | Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration |
| title_full | Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration |
| title_fullStr | Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration |
| title_full_unstemmed | Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration |
| title_short | Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration |
| title_sort | optimizing resource allocation in industrial iot with federated machine learning and edge computing integration |
| topic | Industrial Internet of Things Federated learning Resource allocation Mobile edge computing |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025024570 |
| work_keys_str_mv | AT alaaralshamasneh optimizingresourceallocationinindustrialiotwithfederatedmachinelearningandedgecomputingintegration AT fatenkhalidkarim optimizingresourceallocationinindustrialiotwithfederatedmachinelearningandedgecomputingintegration AT yuwang optimizingresourceallocationinindustrialiotwithfederatedmachinelearningandedgecomputingintegration |