Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture
With the emergence of intelligent services and applications powered by artificial intelligence (AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional approaches to machine learning (ML) relied on centralized data collection and processing, where data was collected an...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10372586/ |
| _version_ | 1797348682613391360 |
|---|---|
| author | Zahra Abbas Sunila Fatima Ahmad Madiha Haider Syed Adeel Anjum Semeen Rehman |
| author_facet | Zahra Abbas Sunila Fatima Ahmad Madiha Haider Syed Adeel Anjum Semeen Rehman |
| author_sort | Zahra Abbas |
| collection | DOAJ |
| description | With the emergence of intelligent services and applications powered by artificial intelligence (AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional approaches to machine learning (ML) relied on centralized data collection and processing, where data was collected and analyzed in one place. However, with the development of Deep Federated Learning (DFL), models can now be trained on decentralized data, reducing the need for centralized data storage and processing. In this work, we provide a detailed analysis of DFL and its benefits, followed by an extensive survey of the use of DFL in various IoT services and applications. We have studied the impact of DFL and how to preserve security and privacy by ensuring compliance in machine learning-enabled IoT systems. In addition, we present a generic architecture for a GDPR-compliant DFL-based framework. Finally, we discuss the existing obstacles and possible future research directions for DFL in IoT. |
| first_indexed | 2024-03-08T12:09:30Z |
| format | Article |
| id | doaj.art-3d6b2479a05d43558b06198f71d489bd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T12:09:30Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3d6b2479a05d43558b06198f71d489bd2024-01-23T00:04:06ZengIEEEIEEE Access2169-35362024-01-0112105481057410.1109/ACCESS.2023.334402910372586Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant ArchitectureZahra Abbas0https://orcid.org/0009-0005-0323-3218Sunila Fatima Ahmad1https://orcid.org/0009-0002-3631-9868Madiha Haider Syed2https://orcid.org/0000-0003-0123-3554Adeel Anjum3Semeen Rehman4https://orcid.org/0000-0002-8972-0949Institute of Information Technology, Quaid-i-Azam University, Islamabad, PakistanInstitute of Information Technology, Quaid-i-Azam University, Islamabad, PakistanInstitute of Information Technology, Quaid-i-Azam University, Islamabad, PakistanInstitute of Information Technology, Quaid-i-Azam University, Islamabad, PakistanInstitute of Computer Technology, Technische Universität Wien (TU Wien), Vienna, AustriaWith the emergence of intelligent services and applications powered by artificial intelligence (AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional approaches to machine learning (ML) relied on centralized data collection and processing, where data was collected and analyzed in one place. However, with the development of Deep Federated Learning (DFL), models can now be trained on decentralized data, reducing the need for centralized data storage and processing. In this work, we provide a detailed analysis of DFL and its benefits, followed by an extensive survey of the use of DFL in various IoT services and applications. We have studied the impact of DFL and how to preserve security and privacy by ensuring compliance in machine learning-enabled IoT systems. In addition, we present a generic architecture for a GDPR-compliant DFL-based framework. Finally, we discuss the existing obstacles and possible future research directions for DFL in IoT.https://ieeexplore.ieee.org/document/10372586/Deep federated learning (DFL)Internet of Things (IoT)artificial intelligence (AI)compliancegeneral data protection regulation (GDPR) |
| spellingShingle | Zahra Abbas Sunila Fatima Ahmad Madiha Haider Syed Adeel Anjum Semeen Rehman Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture IEEE Access Deep federated learning (DFL) Internet of Things (IoT) artificial intelligence (AI) compliance general data protection regulation (GDPR) |
| title | Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture |
| title_full | Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture |
| title_fullStr | Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture |
| title_full_unstemmed | Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture |
| title_short | Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture |
| title_sort | exploring deep federated learning for the internet of things a gdpr compliant architecture |
| topic | Deep federated learning (DFL) Internet of Things (IoT) artificial intelligence (AI) compliance general data protection regulation (GDPR) |
| url | https://ieeexplore.ieee.org/document/10372586/ |
| work_keys_str_mv | AT zahraabbas exploringdeepfederatedlearningfortheinternetofthingsagdprcompliantarchitecture AT sunilafatimaahmad exploringdeepfederatedlearningfortheinternetofthingsagdprcompliantarchitecture AT madihahaidersyed exploringdeepfederatedlearningfortheinternetofthingsagdprcompliantarchitecture AT adeelanjum exploringdeepfederatedlearningfortheinternetofthingsagdprcompliantarchitecture AT semeenrehman exploringdeepfederatedlearningfortheinternetofthingsagdprcompliantarchitecture |