Open Access

REVIEW

Federated Deep Learning in Intelligent Urban Ecosystems: A Systematic Review of Advancements and Applications in Smart Cities, Homes, Buildings, and Healthcare Systems

Muhammad Adnan Tariq¹, Sunawar Khan², Tehseen Mazhar^2,3, Tariq Shahzad⁴, Sahar Arooj⁵, Khmaies Ouahada⁶, Muhammad Adnan Khan^7,*, Habib Hamam^8,9,10,11

1 Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, Pakistan
2 School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
3 Department of Computer Science and Information Technology, School Education Department, Government of Punjab, Layyah, Pakistan
4 Department of Computer Engineering, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, Pakistan
5 Department of Computer Science, Alshifa Institute of Health Sciences, Narowal, Pakistan
6 Department of Electrical and Electronic Engineering Science, University of Johannesburg, Auckland Park, Johannesburg, South Africa
7 Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-Si, Republic of Korea
8 Faculty of Engineering, Université de Moncton, Moncton, NB, Canada
9 School of Electrical Engineering, University of Johannesburg, Johannesburg, South Africa
10 International Institute of Technology and Management (IITG), Av. Grandes Ecoles, Libreville, Gabon
11 Bridges for Academic Excellence—Spectrum, Tunis, Tunisia

* Corresponding Author: Muhammad Adnan Khan. Email:

Computer Modeling in Engineering & Sciences 2026, 146(3), 8 https://doi.org/10.32604/cmes.2026.078672

Received 06 January 2026; Accepted 02 March 2026; Issue published 30 March 2026

Abstract

The contemporary smart cities, smart homes, smart buildings, and smart health care systems are the results of the explosive growth of Internet of Things (IoT) devices and deep learning. Yet the centralized training paradigms have fundamental issues in data privacy, regulatory compliance, and ownership silo alongside the scaled limitations of the real-life application. The concept of Federated Deep Learning (FDL) is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings. It is an overview of the privacy-preserving developments in FDL as of 2018–2025 with a narrow scope on its usage in smart cities (traffic prediction, environmental monitoring, energy grids), smart homes/buildings/IoT (non-intrusive load monitoring, HVAC optimization, anomaly detection) and the healthcare application (medical imaging, Electronic Health Records (EHR) analysis, remote monitoring). It gives coherent taxonomy, domain pipelines, comparative analyses of privacy mechanisms (differential privacy, secure aggregation, Homomorphic Encryption (HE), Trusted Execution Environments (TEEs), blockchain enhanced and hybrids), system structures, security/robustness defense, deployment/Machine Learning Operation (MLOps) issues, and the longstanding challenges (non-IID heterogeneity, communication efficiency, fairness, and sustainability). Some of the contributions made are structured comparisons of privacy threats, practical design advice on urban areas, recognition of open problems, and a research roadmap into the future up to 2035. The paper brings out the transformational worth of FDL in building credible, scalable, and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization, real-world testbeds, and ethical governance.

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Supplementary Material

Supplementary Material File

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