Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks
Federated learning is presented as an effective solution to train artificial intelligence models on the Internet of Things networks without centralizing data, thus preserving privacy and minimizing security risks. However, its implementation in low-power devices, such as Tiny Machine Learning, faces...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10758420/ |
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| author | William Villegas-Ch Rommel Gutierrez Alexandra Maldonado Navarro Aracely Mera-Navarrete |
| author_facet | William Villegas-Ch Rommel Gutierrez Alexandra Maldonado Navarro Aracely Mera-Navarrete |
| author_sort | William Villegas-Ch |
| collection | DOAJ |
| description | Federated learning is presented as an effective solution to train artificial intelligence models on the Internet of Things networks without centralizing data, thus preserving privacy and minimizing security risks. However, its implementation in low-power devices, such as Tiny Machine Learning, faces significant challenges due to the processing, memory, and unstable connectivity limitations that characterize these environments. This study addresses these issues by developing a federated system optimized for Tiny Machine Learning devices, integrating differential privacy and encryption techniques adapted to their constraints. The methodology employed includes the evaluation of model precision and energy consumption in variable communication scenarios, as well as heterogeneous workloads. The results show that federated learning reduces energy consumption by 33% compared to the centralized approach, reaching an average of 100 mW. Furthermore, implementing differential privacy-maintained precision with a loss of only 1.2% demonstrates the feasibility of protecting sensitive data without substantially affecting performance. However, precision dropped by 8% in high communication loss scenarios, underlining the importance of stable connectivity for optimal system performance. |
| format | Article |
| id | doaj-art-a0a8bfa080004eb1a05c98161cd20cb2 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a0a8bfa080004eb1a05c98161cd20cb22025-08-20T02:27:45ZengIEEEIEEE Access2169-35362024-01-011217435417437010.1109/ACCESS.2024.350351610758420Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT NetworksWilliam Villegas-Ch0https://orcid.org/0000-0002-5421-7710Rommel Gutierrez1https://orcid.org/0009-0004-3230-4129Alexandra Maldonado Navarro2Aracely Mera-Navarrete3Escuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, EcuadorEscuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, EcuadorEscuela de Posgrados, Maestría en Derecho Digital, Universidad de Las Américas, Quito, EcuadorDepartamento de Sistemas, Universidad Internacional del Ecuador, Quito, EcuadorFederated learning is presented as an effective solution to train artificial intelligence models on the Internet of Things networks without centralizing data, thus preserving privacy and minimizing security risks. However, its implementation in low-power devices, such as Tiny Machine Learning, faces significant challenges due to the processing, memory, and unstable connectivity limitations that characterize these environments. This study addresses these issues by developing a federated system optimized for Tiny Machine Learning devices, integrating differential privacy and encryption techniques adapted to their constraints. The methodology employed includes the evaluation of model precision and energy consumption in variable communication scenarios, as well as heterogeneous workloads. The results show that federated learning reduces energy consumption by 33% compared to the centralized approach, reaching an average of 100 mW. Furthermore, implementing differential privacy-maintained precision with a loss of only 1.2% demonstrates the feasibility of protecting sensitive data without substantially affecting performance. However, precision dropped by 8% in high communication loss scenarios, underlining the importance of stable connectivity for optimal system performance.https://ieeexplore.ieee.org/document/10758420/Federated learning in IoTtiny machine learning optimizationprivacy differential techniquesenergy efficiency in edge computing |
| spellingShingle | William Villegas-Ch Rommel Gutierrez Alexandra Maldonado Navarro Aracely Mera-Navarrete Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks IEEE Access Federated learning in IoT tiny machine learning optimization privacy differential techniques energy efficiency in edge computing |
| title | Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks |
| title_full | Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks |
| title_fullStr | Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks |
| title_full_unstemmed | Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks |
| title_short | Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks |
| title_sort | optimizing federated learning on tinyml devices for privacy protection and energy efficiency in iot networks |
| topic | Federated learning in IoT tiny machine learning optimization privacy differential techniques energy efficiency in edge computing |
| url | https://ieeexplore.ieee.org/document/10758420/ |
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