<![CDATA[The increasing deployment of intelligent systems across healthcare, industrial automation, smart cities, transportation, and edge-computing environments has intensified concerns regarding privacy, data sovereignty, and secure collaborative learning. This study evaluates the effectiveness of Federated Learning (FL) as a privacy-preserving paradigm capable of supporting distributed intelligence without requiring centralized data collection. An empirical experimental design was implemented using a federated architecture consisting of decentralized client nodes, a central aggregation server, the Federated Averaging algorithm, and integrated secure aggregation with differential privacy mechanisms. Experimental evaluation was conducted through repeated validation under heterogeneous client configurations and varying data distributions. The results demonstrate that the proposed framework achieved strong predictive performance, attaining 93.41% accuracy and 95.28% AUC-ROC while maintaining stable convergence under non-identically distributed data conditions. Security evaluation revealed substantial reductions in model inversion, membership inference, and gradient leakage attacks, confirming the effectiveness of the implemented privacy-preserving mechanisms. Scalability analysis further indicated that the framework maintained reliable performance across expanding client populations with acceptable communication overhead and computational efficiency. The findings confirm that Federated Learning provides a practical and scalable foundation for trustworthy intelligent systems by balancing predictive effectiveness, privacy protection, security resilience, and operational feasibility in distributed environments.]]>
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