Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications
Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments where data privacy, bandwidth constraints, and device heterogeneity are paramount. This survey provides a comprehensive overvi...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Journal of Sensor and Actuator Networks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2224-2708/14/1/9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850080981840560128 |
|---|---|
| author | Elias Dritsas Maria Trigka |
| author_facet | Elias Dritsas Maria Trigka |
| author_sort | Elias Dritsas |
| collection | DOAJ |
| description | Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments where data privacy, bandwidth constraints, and device heterogeneity are paramount. This survey provides a comprehensive overview of FL, focusing on its integration with the IoT. We delve into the motivations behind adopting FL for IoT, the underlying techniques that facilitate this integration, the unique challenges posed by IoT environments, and the diverse range of applications where FL is making an impact. Finally, this submission also outlines future research directions and open issues, aiming to provide a detailed roadmap for advancing FL in IoT settings. |
| format | Article |
| id | doaj-art-8d63d7cdda4a44afa4dfc137098326d1 |
| institution | DOAJ |
| issn | 2224-2708 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj-art-8d63d7cdda4a44afa4dfc137098326d12025-08-20T02:44:50ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082025-01-01141910.3390/jsan14010009Federated Learning for IoT: A Survey of Techniques, Challenges, and ApplicationsElias Dritsas0Maria Trigka1Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, GreeceIndustrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, GreeceFederated Learning (FL) has emerged as a pivotal approach for decentralized Machine Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments where data privacy, bandwidth constraints, and device heterogeneity are paramount. This survey provides a comprehensive overview of FL, focusing on its integration with the IoT. We delve into the motivations behind adopting FL for IoT, the underlying techniques that facilitate this integration, the unique challenges posed by IoT environments, and the diverse range of applications where FL is making an impact. Finally, this submission also outlines future research directions and open issues, aiming to provide a detailed roadmap for advancing FL in IoT settings.https://www.mdpi.com/2224-2708/14/1/9federated learningInternet of Thingsmachine learningprivacy-preservingcommunication efficiency |
| spellingShingle | Elias Dritsas Maria Trigka Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications Journal of Sensor and Actuator Networks federated learning Internet of Things machine learning privacy-preserving communication efficiency |
| title | Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications |
| title_full | Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications |
| title_fullStr | Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications |
| title_full_unstemmed | Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications |
| title_short | Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications |
| title_sort | federated learning for iot a survey of techniques challenges and applications |
| topic | federated learning Internet of Things machine learning privacy-preserving communication efficiency |
| url | https://www.mdpi.com/2224-2708/14/1/9 |
| work_keys_str_mv | AT eliasdritsas federatedlearningforiotasurveyoftechniqueschallengesandapplications AT mariatrigka federatedlearningforiotasurveyoftechniqueschallengesandapplications |