My Publications

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, Trusted Execution Environments (TEEs), blockchain enhanced and hybrids), system structures, security/robustness defense, deployment/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.