<![CDATA[The rapid expansion of smart ecosystems driven by Internet of Things (IoT) technologies requires real-time data processing that is secure, efficient, and capable of handling large-scale heterogeneous data streams. Traditional centralized learning approaches often struggle with latency, bandwidth constraints, and privacy risks due to the continuous transfer of raw data. This study aims to optimize smart ecosystem performance by integrating Federated Learning (FL) as a distributed intelligence framework for real-time IoT data processing. The proposed model allows multiple IoT nodes to collaboratively train a global model without sharing raw data, thereby enhancing privacy protection and reducing network load. Experimental results demonstrate that FL provides higher accuracy, better scalability, and improved resilience compared to centralized systems, especially in environments with diverse sensor distributions. These findings confirm that Federated Learning is a highly promising approach for strengthening smart ecosystem reliability, responsiveness, and data security. This research contributes to developing future-ready IoT architectures capable of supporting intelligent, decentralized, and privacy-preserving environments.]]>
Copyrights © 2026
