Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical Clustering
Federated learning (FL) has emerged as a promising solution for the Internet of Things (IoT), facilitating distributed artificial intelligence while ensuring communication efficiency and data privacy. Traditional methods involve transmitting raw sensory data from IoT devices to servers or base-stati...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/1/4 |
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| author | M. Baqer |
| author_facet | M. Baqer |
| author_sort | M. Baqer |
| collection | DOAJ |
| description | Federated learning (FL) has emerged as a promising solution for the Internet of Things (IoT), facilitating distributed artificial intelligence while ensuring communication efficiency and data privacy. Traditional methods involve transmitting raw sensory data from IoT devices to servers or base-stations for processing, resulting in significant communication overhead. This overhead not only increases energy consumption but also diminishes device longevity within IoT networks. By focusing on model updates rather than raw data transmission, FL reduces the volume of data communicated to the base-station; however, FL still faces challenges due to the multiple communication rounds required for convergence. This research introduces an innovative approach that leverages the in-network processing capabilities of IoT devices by integrating a hierarchical clustering routing protocol with FL. This approach enhances energy efficiency through single-round pattern recognition, minimizing the need for multiple communication rounds to achieve convergence. It is envisaged that the proposed approach will prolong the lifespan of IoT devices and maintain high accuracy in event detection, all while ensuring robust data privacy. |
| format | Article |
| id | doaj-art-aa74f0dbd0cd4126a1f18a43c6751314 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-aa74f0dbd0cd4126a1f18a43c67513142025-01-24T13:33:32ZengMDPI AGFuture Internet1999-59032024-12-01171410.3390/fi17010004Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical ClusteringM. Baqer0Department of Computer Engineering, College of Information Technology, University of Bahrain, Sakhair P.O. Box 32038, BahrainFederated learning (FL) has emerged as a promising solution for the Internet of Things (IoT), facilitating distributed artificial intelligence while ensuring communication efficiency and data privacy. Traditional methods involve transmitting raw sensory data from IoT devices to servers or base-stations for processing, resulting in significant communication overhead. This overhead not only increases energy consumption but also diminishes device longevity within IoT networks. By focusing on model updates rather than raw data transmission, FL reduces the volume of data communicated to the base-station; however, FL still faces challenges due to the multiple communication rounds required for convergence. This research introduces an innovative approach that leverages the in-network processing capabilities of IoT devices by integrating a hierarchical clustering routing protocol with FL. This approach enhances energy efficiency through single-round pattern recognition, minimizing the need for multiple communication rounds to achieve convergence. It is envisaged that the proposed approach will prolong the lifespan of IoT devices and maintain high accuracy in event detection, all while ensuring robust data privacy.https://www.mdpi.com/1999-5903/17/1/4federated learninginternet of thingssensor networksartificial intelligencemachine learningdistributed learning |
| spellingShingle | M. Baqer Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical Clustering Future Internet federated learning internet of things sensor networks artificial intelligence machine learning distributed learning |
| title | Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical Clustering |
| title_full | Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical Clustering |
| title_fullStr | Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical Clustering |
| title_full_unstemmed | Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical Clustering |
| title_short | Energy-Efficient Federated Learning for Internet of Things: Leveraging In-Network Processing and Hierarchical Clustering |
| title_sort | energy efficient federated learning for internet of things leveraging in network processing and hierarchical clustering |
| topic | federated learning internet of things sensor networks artificial intelligence machine learning distributed learning |
| url | https://www.mdpi.com/1999-5903/17/1/4 |
| work_keys_str_mv | AT mbaqer energyefficientfederatedlearningforinternetofthingsleveraginginnetworkprocessingandhierarchicalclustering |