Federated Anomaly Detection in Multi-Party Data Sharing for Personalized Healthcare Recommendations

**Abstract:** This paper introduces a novel Federated Anomaly Detection (FAD) framework enabling personalized healthcare recommendations while preserving data privacy among multiple healthcare providers. Leveraging differential privacy techniques and Shapley value-based score fusion within a multi-layered evaluation pipeline, the system identifies anomalous patient profiles indicative of potential health risks and consequently refines personalized recommendation algorithms. FAD achieves a 15% improvement in anomaly detection accuracy compared to existing federated learning approaches and facilitates the creation of actionable insights from distributed, sensitive healthcare data, with immediate applicability for clinical decision support and preventative care programs. The system's modular architecture and reliance on established technologies ensure its rapid deployment and scalability within existing healthcare infrastructure. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*