A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare

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The ever-evolving domain of machine learning has witnessed significant advancements with the advent of federated learning, a paradigm revered for its capacity to facilitate model training on decentralized data sources while upholding data confidentiality. This research introduces a federated learning-based framework designed to address gaps in existing smoking prediction models, which often compromise privacy and lack data generalizability. By utilizing a distributed approach, the framework ensures secure, privacy-preserved model training on decentralized devices, enabling the capture of diverse smoking behavior patterns. The proposed framework incorporates careful data preprocessing, rational model architecture selection, and optimal parameter tuning to predict smoking with high precision. The results demonstrate the efficacy of the model, achieving an accuracy rate of 97.65%, complemented by an F1-score of 97.41%, precision of 97.31%, and recall rate of 97.36%, significantly outperforming traditional approaches. This research also discusses the benefits of federated learning, including efficient time management, parallel processing, secure model updates, and enhanced data privacy, while addressing limitations such as computational overhead. These findings underscore the transformative potential of federated learning in healthcare, paving the way for future advancements in privacy-preserved predictive modeling.