Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering
Abstract In the rapidly growing Internet of Things (IoT) landscape, federated learning (FL) plays a crucial role in enhancing the performance of heterogeneous edge computing environments due to its scalability, robustness, and low energy consumption. However, one of the major challenges in such envi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78239-z |
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| author | Fahad Razaque Mughal Jingsha He Bhagwan Das Fayaz Ali Dharejo Nafei Zhu Surbhi Bhatia Khan Saeed Alzahrani |
| author_facet | Fahad Razaque Mughal Jingsha He Bhagwan Das Fayaz Ali Dharejo Nafei Zhu Surbhi Bhatia Khan Saeed Alzahrani |
| author_sort | Fahad Razaque Mughal |
| collection | DOAJ |
| description | Abstract In the rapidly growing Internet of Things (IoT) landscape, federated learning (FL) plays a crucial role in enhancing the performance of heterogeneous edge computing environments due to its scalability, robustness, and low energy consumption. However, one of the major challenges in such environments is the efficient selection of edge nodes and the optimization of resource allocation, especially in dynamic and resource-constrained settings. To address this, we propose a novel architecture called Multi-Edge Clustered and Edge AI Heterogeneous Federated Learning (MEC-AI HetFL), which leverages multi-edge clustering and AI-driven node communication. This architecture enables edge AI nodes to collaborate, dynamically selecting significant nodes and optimizing global learning tasks with low complexity. Compared to existing solutions like EdgeFed, FedSA, FedMP, and H-DDPG, MEC-AI HetFL improves resource allocation, quality score, and learning accuracy, offering up to 5 times better performance in heterogeneous and distributed environments. The solution is validated through simulations and network traffic tests, demonstrating its ability to address the key challenges in IoT edge computing deployments. |
| format | Article |
| id | doaj-art-4979d87aa7f540d59d48df5b2bdca758 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4979d87aa7f540d59d48df5b2bdca7582024-11-24T12:22:25ZengNature PortfolioScientific Reports2045-23222024-11-0114111910.1038/s41598-024-78239-zAdaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clusteringFahad Razaque Mughal0Jingsha He1Bhagwan Das2Fayaz Ali Dharejo3Nafei Zhu4Surbhi Bhatia Khan5Saeed Alzahrani6Faculty of Information Technology, Beijing University of TechnologyFaculty of Information Technology, Beijing University of TechnologyCentre for Artificial Intelligence Research and Optimization (AIRO), Design and Creative Technology Vertical, Torrens UniversityComputer Vision Lab, CAIDAS, IFI, University of WurzburgFaculty of Information Technology, Beijing University of TechnologySchool of Science, Engineering and Environment, University of SalfordManagement Information System, King Saud UniversityAbstract In the rapidly growing Internet of Things (IoT) landscape, federated learning (FL) plays a crucial role in enhancing the performance of heterogeneous edge computing environments due to its scalability, robustness, and low energy consumption. However, one of the major challenges in such environments is the efficient selection of edge nodes and the optimization of resource allocation, especially in dynamic and resource-constrained settings. To address this, we propose a novel architecture called Multi-Edge Clustered and Edge AI Heterogeneous Federated Learning (MEC-AI HetFL), which leverages multi-edge clustering and AI-driven node communication. This architecture enables edge AI nodes to collaborate, dynamically selecting significant nodes and optimizing global learning tasks with low complexity. Compared to existing solutions like EdgeFed, FedSA, FedMP, and H-DDPG, MEC-AI HetFL improves resource allocation, quality score, and learning accuracy, offering up to 5 times better performance in heterogeneous and distributed environments. The solution is validated through simulations and network traffic tests, demonstrating its ability to address the key challenges in IoT edge computing deployments.https://doi.org/10.1038/s41598-024-78239-zInternet of thingsResource managementEdge artificial intelligenceHeterogeneous cluster networksEdge computingFederated learning |
| spellingShingle | Fahad Razaque Mughal Jingsha He Bhagwan Das Fayaz Ali Dharejo Nafei Zhu Surbhi Bhatia Khan Saeed Alzahrani Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering Scientific Reports Internet of things Resource management Edge artificial intelligence Heterogeneous cluster networks Edge computing Federated learning |
| title | Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering |
| title_full | Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering |
| title_fullStr | Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering |
| title_full_unstemmed | Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering |
| title_short | Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering |
| title_sort | adaptive federated learning for resource constrained iot devices through edge intelligence and multi edge clustering |
| topic | Internet of things Resource management Edge artificial intelligence Heterogeneous cluster networks Edge computing Federated learning |
| url | https://doi.org/10.1038/s41598-024-78239-z |
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