<![CDATA[The rapid growth of smart applications across domains—ranging from healthcare and finance to personalized education—has intensified concerns about data privacy and model scalability. Federated Learning (FL) offers a promising framework by enabling distributed model training without sharing raw data, yet conventional FL approaches struggle with challenges such as heterogeneous data distributions, limited device resources, and dynamic network conditions. This paper introduces an Adaptive Federated Learning (AFL) framework designed to address these limitations while preserving user privacy. The proposed AFL dynamically adjusts aggregation strategies, learning rates, and participation levels based on client performance metrics and resource availability. We integrate differential privacy mechanisms and secure aggregation to ensure robust privacy guarantees without compromising model accuracy. Experimental evaluations on benchmark smart application datasets—including IoT sensor data and mobile user behavior logs—demonstrate that AFL achieves up to 15–20% improvement in convergence speed and notable reductions in communication overhead compared to standard FL methods. Our findings highlight AFL’s potential as a scalable and privacy-preserving solution for next-generation smart applications, paving the way for more secure and adaptive AI ecosystems.]]>
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