Cloudlet Federation Based Context-Aware Federated Learning Approach
A Cloudlet federation can be beneficial to overcome the latency and resource scarcity challenges in a cloudlet deployment altogether, as a task can run on a cloudlet within the federation, sharing resources of member cloudlets. Nonetheless, the cloudlet federation is not context-aware in terms of la...
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| Format: | Article |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9912422/ |
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| author | Sana Latif Muhammad Ziad Nayyer Imran Raza Syed Asad Hussain M. Hasan Jamal Soojung Hur Imran Ashraf |
| author_facet | Sana Latif Muhammad Ziad Nayyer Imran Raza Syed Asad Hussain M. Hasan Jamal Soojung Hur Imran Ashraf |
| author_sort | Sana Latif |
| collection | DOAJ |
| description | A Cloudlet federation can be beneficial to overcome the latency and resource scarcity challenges in a cloudlet deployment altogether, as a task can run on a cloudlet within the federation, sharing resources of member cloudlets. Nonetheless, the cloudlet federation is not context-aware in terms of latency, so to perform federated learning in cloudlet federation, the selection of a resource-efficient deep learning model is challenging. Additionally, the accuracy of a deep learning model can be affected if end-user devices are unreliable and provide incorrect data for training deep learning models at the cloudlets. Thus, resource and context-aware federated learning solutions are required for accurate and latency-critical applications such as COVID-19 detection using X-ray images. This paper presents a novel context-aware cloudlet federated learning solution for COVID-19 detection that monitors the resources of a cloudlet using a broker thereby minimizing latency without any impact on the accuracy of the deep learning model. Results show that the proposed model reduces the latency by 5% and increases the accuracy by 5% as compared to the state-of-the-art conventional federated learning approach. |
| format | Article |
| id | doaj-art-aa2d9193a73546f7b60bc99d6a394a50 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-aa2d9193a73546f7b60bc99d6a394a502025-08-20T02:10:06ZengIEEEIEEE Access2169-35362022-01-011010915310916610.1109/ACCESS.2022.32125509912422Cloudlet Federation Based Context-Aware Federated Learning ApproachSana Latif0https://orcid.org/0000-0002-6213-3576Muhammad Ziad Nayyer1https://orcid.org/0000-0001-6014-4397Imran Raza2https://orcid.org/0000-0003-3118-4634Syed Asad Hussain3https://orcid.org/0000-0002-8264-7857M. Hasan Jamal4https://orcid.org/0000-0002-0114-0887Soojung Hur5Imran Ashraf6https://orcid.org/0000-0002-8271-6496Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanDepartment of Computer Science, GIFT University, Gujranwala, PakistanDepartment of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanDepartment of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanDepartment of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaA Cloudlet federation can be beneficial to overcome the latency and resource scarcity challenges in a cloudlet deployment altogether, as a task can run on a cloudlet within the federation, sharing resources of member cloudlets. Nonetheless, the cloudlet federation is not context-aware in terms of latency, so to perform federated learning in cloudlet federation, the selection of a resource-efficient deep learning model is challenging. Additionally, the accuracy of a deep learning model can be affected if end-user devices are unreliable and provide incorrect data for training deep learning models at the cloudlets. Thus, resource and context-aware federated learning solutions are required for accurate and latency-critical applications such as COVID-19 detection using X-ray images. This paper presents a novel context-aware cloudlet federated learning solution for COVID-19 detection that monitors the resources of a cloudlet using a broker thereby minimizing latency without any impact on the accuracy of the deep learning model. Results show that the proposed model reduces the latency by 5% and increases the accuracy by 5% as compared to the state-of-the-art conventional federated learning approach.https://ieeexplore.ieee.org/document/9912422/Cloudlet federationfederated learningedge computingcloud computing |
| spellingShingle | Sana Latif Muhammad Ziad Nayyer Imran Raza Syed Asad Hussain M. Hasan Jamal Soojung Hur Imran Ashraf Cloudlet Federation Based Context-Aware Federated Learning Approach IEEE Access Cloudlet federation federated learning edge computing cloud computing |
| title | Cloudlet Federation Based Context-Aware Federated Learning Approach |
| title_full | Cloudlet Federation Based Context-Aware Federated Learning Approach |
| title_fullStr | Cloudlet Federation Based Context-Aware Federated Learning Approach |
| title_full_unstemmed | Cloudlet Federation Based Context-Aware Federated Learning Approach |
| title_short | Cloudlet Federation Based Context-Aware Federated Learning Approach |
| title_sort | cloudlet federation based context aware federated learning approach |
| topic | Cloudlet federation federated learning edge computing cloud computing |
| url | https://ieeexplore.ieee.org/document/9912422/ |
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