Federated Learning and Its Role in the Privacy Preservation of IoT Devices
Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/14/9/246 |
| _version_ | 1827660852775354368 |
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| author | Tanweer Alam Ruchi Gupta |
| author_facet | Tanweer Alam Ruchi Gupta |
| author_sort | Tanweer Alam |
| collection | DOAJ |
| description | Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results. |
| first_indexed | 2024-03-09T23:58:25Z |
| format | Article |
| id | doaj.art-739821a8bf8b4e82b524bed207e405ce |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T23:58:25Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-739821a8bf8b4e82b524bed207e405ce2023-11-23T16:20:30ZengMDPI AGFuture Internet1999-59032022-08-0114924610.3390/fi14090246Federated Learning and Its Role in the Privacy Preservation of IoT DevicesTanweer Alam0Ruchi Gupta1Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi ArabiaDepartment of Computer Science, Ajay Kumar Garg Engineering College, Ghaziabad 201015, IndiaFederated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results.https://www.mdpi.com/1999-5903/14/9/246federated learningartificial intelligenceprivacysecuritymachine learning |
| spellingShingle | Tanweer Alam Ruchi Gupta Federated Learning and Its Role in the Privacy Preservation of IoT Devices Future Internet federated learning artificial intelligence privacy security machine learning |
| title | Federated Learning and Its Role in the Privacy Preservation of IoT Devices |
| title_full | Federated Learning and Its Role in the Privacy Preservation of IoT Devices |
| title_fullStr | Federated Learning and Its Role in the Privacy Preservation of IoT Devices |
| title_full_unstemmed | Federated Learning and Its Role in the Privacy Preservation of IoT Devices |
| title_short | Federated Learning and Its Role in the Privacy Preservation of IoT Devices |
| title_sort | federated learning and its role in the privacy preservation of iot devices |
| topic | federated learning artificial intelligence privacy security machine learning |
| url | https://www.mdpi.com/1999-5903/14/9/246 |
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