{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T20:56:47Z","timestamp":1783457807088,"version":"3.55.0"},"reference-count":180,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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\u2019s potential in driving innovative, secure, and efficient intelligence for the next generation of smart buildings.<\/jats:p>","DOI":"10.3390\/computers14040124","type":"journal-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:41:02Z","timestamp":1743079262000},"page":"124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond"],"prefix":"10.3390","volume":"14","author":[{"given":"Mohamed Rafik Aymene","family":"Berkani","sequence":"first","affiliation":[{"name":"Research Laboratory in Advanced Electronics Systems (LSEA), University Yahia Fares of Medea, Medea 26000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ammar","family":"Chouchane","sequence":"additional","affiliation":[{"name":"University Center of Barika, Amdoukal Road, Barika 05001, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8904-5587","authenticated-orcid":false,"given":"Yassine","family":"Himeur","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3048-8118","authenticated-orcid":false,"given":"Abdelmalik","family":"Ouamane","sequence":"additional","affiliation":[{"name":"Laboratory of LI3C, University of Biskra, Biskra 07000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sami","family":"Miniaoui","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3017-9243","authenticated-orcid":false,"given":"Shadi","family":"Atalla","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2784-5188","authenticated-orcid":false,"given":"Wathiq","family":"Mansoor","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hussain","family":"Al-Ahmad","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125458","DOI":"10.1016\/j.apenergy.2025.125458","article-title":"Continual learning for energy management systems: A review of methods and applications, and a case study","volume":"384","author":"Sayed","year":"2025","journal-title":"Appl. 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