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Healthcare billing fraud imposes major financial losses globally, costing public and private payers hundreds of billions annually. It exploits fragmented healthcare payment systems where multiple insurers process overlapping patient populations without coordination, creating blind spots that enable sophisticated cross-payer fraud schemes. Individual payers cannot detect patterns such as duplicate billing across Medicare and commercial insurers because current detection models operate within isolated organizational and regulatory boundaries. Strict privacy laws like HIPAA and GDPR further prevent sharing patient-level claims data, limiting centralized analytics. To address this, a federated anomaly detection framework is proposed in which autoencoders are trained locally at each payer without exchanging raw data. Each institution learns normal billing patterns through reconstruction-based unsupervised learning and identifies anomalies via reconstruction error. A central server aggregates encoder parameters using FedAvg, optionally with differential privacy, to build a globally informed model while preserving data locality. The resulting system enables detection of cross-payer fraud patterns, such as double billing and unbundling, that single-payer systems miss, while transmitting only model parameters through secure channels. This approach provides a privacy-preserving, scalable solution for multi-payer healthcare fraud detection under strict regulatory constraints.

During the COVID-19 pandemic, machine learning models developed in high-resource hospitals achieved strong performance in predicting outcomes such as mortality, ICU admission, and mechanical ventilation, but their accuracy often degrades when applied to low-resource settings due to differences in patient populations, disease severity, clinical practices, and documentation quality. Low-resource hospitals also face limited patient volumes, incomplete labeled data, and strict privacy regulations (e.g., HIPAA and GDPR), which prevent centralized data sharing and hinder independent model development, creating a barrier to equitable AI deployment. To address this, we propose a federated transfer learning framework that adapts prognostic models from high-resource to low-resource hospitals without exchanging patient-level data. The approach transfers only aggregate statistics (e.g., feature means, variances, class-conditional distributions, and correlations) via a secure lightweight protocol, enabling target hospitals to align feature distributions using domain adaptation techniques and fine-tune models on small local datasets. The framework includes source model training, statistical aggregation, secure transmission, and target-side adaptation modules, ensuring no raw patient data leaves any institution. By relying on aggregate statistics, the method preserves privacy while mitigating domain shift and maintaining clinical utility across diverse healthcare environments. This scalable and privacy-preserving framework supports broader deployment of COVID-19 predictive models and provides a generalizable strategy for other medical conditions with heterogeneous healthcare settings.