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

This comprehensive review examines Federated Deep Learning (FDL) as a privacy-by-design approach for intelligent urban ecosystems. It systematically covers applications across smart cities (traffic prediction, environmental monitoring, energy grids), smart homes/buildings/IoT (non-intrusive load monitoring, HVAC optimization, anomaly detection), and healthcare (medical imaging, EHR analysis, remote monitoring). The paper addresses fundamental research questions on privacy-preserving methods, algorithms for non-IID data, real-world deployment limitations, and open challenges for scalable, sustainable urban AI systems.

This review adopts a systematic review method to aggregate the state-of-the-art in Federated Deep Learning (FDL) in intelligent urban ecosystems. Literature searches were conducted in IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and PubMed (for healthcare-related FDL). Search queries combined core terms (federated deep learning OR federated learning) with domain-specific terms (smart city OR smart cities, smart home OR smart building OR smart buildings) and privacy/security terms (differential privacy, secure aggregation, homomorphic encryption, trusted execution environment, blockchain). Boolean operators and phrase searches were used; the time window was 2018–2025, with emphasis on high-impact works from 2019–2025. Inclusion criteria: papers explicitly discussing FL/FDL with privacy or security measures and relevance to urban ecosystems (smart cities, homes/buildings/IoT, or healthcare). Exclusion criteria: non-urban FL applications, work without methodology or experimental validation, and papers without quantitative assessment. This process yielded approximately 174 high-quality references, organized through a PRISMA-style study selection flow and coherent taxonomy of FDL across urban domains.

The review demonstrates substantial progress in FDL for intelligent urban ecosystems from 2018–2025. Algorithmic advances (FedProx, SCAFFOLD, and variants) improve convergence by 15–20% in heterogeneous urban settings. Hierarchical edge–fog–cloud architectures reduce communication latency by 30–50% in large IoT federations, while energy-efficient methods yield 40–70% energy savings in urban IoT deployments. Domain-specific applications show concrete efficacy: spatio-temporal graph neural networks for traffic prediction on benchmarks like METR-LA; federated CNN/U-Net variants for multi-institutional medical imaging on BraTS; and LSTMs for non-intrusive load monitoring in smart buildings. Hybrid privacy approaches combining differential privacy and secure aggregation provide strong privacy guarantees. Nevertheless, several limitations persist: non-IID data distributions cause client drift and accuracy drops; communication overhead remains high (~100× bandwidth before compression); adversarial attacks can reduce global aggregation accuracy by 30–50%; fairness gaps produce 15–25% performance disparities across demographic groups; and energy consumption on battery-powered devices is 2–5× higher per epoch than centralized training. Pilot deployments in Singapore's traffic monitoring and multi-hospital imaging illustrate viability but highlight the need for standardized testbeds and MLOps frameworks.

Federated deep learning has become an irreplaceable paradigm of privacy-preserving intelligence in interconnected urban ecosystems, bridging the enormous possibilities of deep neural networks with the urgent demands of data sovereignty, regulatory compliance, and scalability. This review outlines how developments from 2018–2025 have enabled substantive applications in smart cities, residential and commercial buildings, IoT, and healthcare—from spatio-temporal traffic forecasting to energy-efficient building management, secure cross-institutional medical imaging, and remote patient monitoring. By maintaining data locality while leveraging collaborative training, FDL helps overcome fundamental shortcomings of centralized systems and is better suited to heterogeneous, privacy-conscious urban environments. However, formidable challenges remain: statistical and system heterogeneity, communication bottlenecks, security vulnerabilities, deficits in fairness and explainability, and energy sustainability concerns. Progress in layered privacy mechanisms, robust security defenses, and domain-specific MLOps offers a solid foundation, but the road ahead requires concerted efforts in standardization, realistic benchmarking, ethical governance, and integration with emerging technologies such as 6G, edge intelligence, and privacy-aware large language models. Looking toward 2035, federated deep learning has transformative potential for resilient, equitable, and sustainable urban intelligence. Realizing this vision will require ongoing interdisciplinary collaboration among researchers, policymakers, industry, and urban planners to develop certified, robust, carbon-aware, and publicly accountable federated systems. Ultimately, FDL not only protects individual privacy but also enables harnessing collective intelligence, opening the doors to smarter, safer, and more inclusive cities on a global scale.