Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond
Federated Learning (FL) is a transformative decentralized approach in machine learning and deep learning, offering enhanced privacy, scalability, and data security. This review paper explores the foundational concepts, and architectural variations of FL, prominent aggregation algorithms like FedAvg,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/4/124 |
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| author | Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Abdelmalik Ouamane Sami Miniaoui Shadi Atalla Wathiq Mansoor Hussain Al-Ahmad |
| author_facet | Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Abdelmalik Ouamane Sami Miniaoui Shadi Atalla Wathiq Mansoor Hussain Al-Ahmad |
| author_sort | Mohamed Rafik Aymene Berkani |
| collection | DOAJ |
| description | Federated Learning (FL) is a transformative decentralized approach in machine learning and deep learning, offering enhanced privacy, scalability, and data security. This review paper explores the foundational concepts, and architectural variations of FL, prominent aggregation algorithms like FedAvg, FedProx, and FedMA, and diverse innovative applications in thermal comfort optimization, energy prediction, healthcare, and anomaly detection within smart buildings. By enabling collaborative model training without centralizing sensitive data, FL ensures privacy and robust performance across heterogeneous environments. We further discuss the integration of FL with advanced technologies, including digital twins and 5G/6G networks, and demonstrate its potential to revolutionize real-time monitoring, and optimize resources. Despite these advances, FL still faces challenges, such as communication overhead, security issues, and non-IID data handling. Future research directions highlight the development of adaptive learning methods, robust privacy measures, and hybrid architectures to fully leverage FL’s potential in driving innovative, secure, and efficient intelligence for the next generation of smart buildings. |
| format | Article |
| id | doaj-art-b49629ae03544cb98a2009a776c125fd |
| institution | DOAJ |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-b49629ae03544cb98a2009a776c125fd2025-08-20T03:13:54ZengMDPI AGComputers2073-431X2025-03-0114412410.3390/computers14040124Advances in Federated Learning: Applications and Challenges in Smart Building Environments and BeyondMohamed Rafik Aymene Berkani0Ammar Chouchane1Yassine Himeur2Abdelmalik Ouamane3Sami Miniaoui4Shadi Atalla5Wathiq Mansoor6Hussain Al-Ahmad7Research Laboratory in Advanced Electronics Systems (LSEA), University Yahia Fares of Medea, Medea 26000, AlgeriaUniversity Center of Barika, Amdoukal Road, Barika 05001, AlgeriaCollege of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab EmiratesLaboratory of LI3C, University of Biskra, Biskra 07000, AlgeriaCollege of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab EmiratesCollege of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab EmiratesCollege of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab EmiratesCollege of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab EmiratesFederated Learning (FL) is a transformative decentralized approach in machine learning and deep learning, offering enhanced privacy, scalability, and data security. This review paper explores the foundational concepts, and architectural variations of FL, prominent aggregation algorithms like FedAvg, FedProx, and FedMA, and diverse innovative applications in thermal comfort optimization, energy prediction, healthcare, and anomaly detection within smart buildings. By enabling collaborative model training without centralizing sensitive data, FL ensures privacy and robust performance across heterogeneous environments. We further discuss the integration of FL with advanced technologies, including digital twins and 5G/6G networks, and demonstrate its potential to revolutionize real-time monitoring, and optimize resources. Despite these advances, FL still faces challenges, such as communication overhead, security issues, and non-IID data handling. Future research directions highlight the development of adaptive learning methods, robust privacy measures, and hybrid architectures to fully leverage FL’s potential in driving innovative, secure, and efficient intelligence for the next generation of smart buildings.https://www.mdpi.com/2073-431X/14/4/124federated Learningdeep learningmachine learningInternet of Thingssmart buildinganomaly detection |
| spellingShingle | Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Abdelmalik Ouamane Sami Miniaoui Shadi Atalla Wathiq Mansoor Hussain Al-Ahmad Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond Computers federated Learning deep learning machine learning Internet of Things smart building anomaly detection |
| title | Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond |
| title_full | Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond |
| title_fullStr | Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond |
| title_full_unstemmed | Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond |
| title_short | Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond |
| title_sort | advances in federated learning applications and challenges in smart building environments and beyond |
| topic | federated Learning deep learning machine learning Internet of Things smart building anomaly detection |
| url | https://www.mdpi.com/2073-431X/14/4/124 |
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