The role of federated learning in improving predictive analytics in public health data systems without compromising privacy

The growing use of data-driven methods in the public health sector has augmented the need for quality and heterogeneous data sets to drive predictive analytics. However, there are significant privacy implications and regulatory and technological risks related to using centralized data systems, especially when handling sensitive data such as health records. This conceptual review addresses how the Federated Learning (FL) concept can revolutionize and make privacy-preserving predictive analytics realizable in the context of public health data systems. FL uses a decentralized model training method with the possibility of many institutions coming together and creating strong analytical models without exchanging raw information. The paper synthesises the main theoretical premises, e.g., privacy-by-design principles, regulatory frameworks, e.g., GDPR and HIPAA, and the main mechanics of FL, e.g., cross-silo and cross-device architecture. It considers the nature of how its privacy-utility trade-off is addressed. It reviews a conceptual framework that could depict the concept of FL integration into government public health systems. It is also possible to mention among the possibilities of FL that it might be used with other promising technologies, such as blockchain and AI, to enhance both outbreak prediction and responsiveness of the health system. Upon declaring numerous opportunities mushrooming, the challenges raised include data heterogeneity, communication overheads, and infrastructural constraints, particularly in low- and middle-income countries that are subjected to serious analysis. The paper ends with recommendations on the policy and system thresholds and directions of future empirical research and conceptual development on implementing FL in the public health sector. Presenting FL as a technology and ethical innovation, this review outlines what changes it can bring to how the public health systems use data and why, resulting in trust, transparency, and regulatory compliance.