Next Article in Journal
Introducing a New Genetic Operator Based on Differential Evolution for the Effective Training of Neural Networks
Previous Article in Journal
Cross-Dataset Data Augmentation Using UMAP for Deep Learning-Based Wind Speed Prediction
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Computers
Volume 14
Issue 4
10.3390/computers14040124
computers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond

by
Mohamed Rafik Aymene Berkani
1,
Ammar Chouchane
2,
Yassine Himeur
3,*,
Abdelmalik Ouamane
4,
Sami Miniaoui
3,
Shadi Atalla
3,
Wathiq Mansoor
3 and
Hussain Al-Ahmad
3
1
Research Laboratory in Advanced Electronics Systems (LSEA), University Yahia Fares of Medea, Medea 26000, Algeria
2
University Center of Barika, Amdoukal Road, Barika 05001, Algeria
3
College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates
4
Laboratory of LI3C, University of Biskra, Biskra 07000, Algeria
*
Author to whom correspondence should be addressed.
Computers 2025, 14(4), 124; https://doi.org/10.3390/computers14040124
Submission received: 23 January 2025 / Revised: 13 March 2025 / Accepted: 19 March 2025 / Published: 27 March 2025
Browse Figures
Versions Notes

Abstract

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.
Keywords: federated Learning; deep learning; machine learning; Internet of Things; smart building; anomaly detection; thermal comfort; energy prediction; healthcare; privacy-preserving federated Learning; deep learning; machine learning; Internet of Things; smart building; anomaly detection; thermal comfort; energy prediction; healthcare; privacy-preserving

Share and Cite

MDPI and ACS Style

Berkani, M.R.A.; Chouchane, A.; Himeur, Y.; Ouamane, A.; Miniaoui, S.; Atalla, S.; Mansoor, W.; Al-Ahmad, H. Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond. Computers 2025, 14, 124. https://doi.org/10.3390/computers14040124

AMA Style

Berkani MRA, Chouchane A, Himeur Y, Ouamane A, Miniaoui S, Atalla S, Mansoor W, Al-Ahmad H. Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond. Computers. 2025; 14(4):124. https://doi.org/10.3390/computers14040124

Chicago/Turabian Style

Berkani, Mohamed Rafik Aymene, Ammar Chouchane, Yassine Himeur, Abdelmalik Ouamane, Sami Miniaoui, Shadi Atalla, Wathiq Mansoor, and Hussain Al-Ahmad. 2025. "Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond" Computers 14, no. 4: 124. https://doi.org/10.3390/computers14040124

APA Style

Berkani, M. R. A., Chouchane, A., Himeur, Y., Ouamane, A., Miniaoui, S., Atalla, S., Mansoor, W., & Al-Ahmad, H. (2025). Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond. Computers, 14(4), 124. https://doi.org/10.3390/computers14040124

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop