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Federated learning (FL) is promoted as a privacy-preserving method for training machine learning models across healthcare institutions without sharing patient data, with growing use in medical imaging, electronic health records, and rare disease research. This critical review examines FL studies from 2017–2024, focusing on privacy guarantees, statistical heterogeneity, communication efficiency, and real-world clinical deployment. A structured search of PubMed, IEEE Xplore, arXiv, and Google Scholar was conducted using relevant FL and healthcare terms, including studies addressing privacy, heterogeneity, communication, or deployment. Reported privacy guarantees are often overstated, with most studies relying on FedAvg without differential privacy. Statistical heterogeneity in non-IID settings remains largely unresolved. Fewer than 5% of studies report real-world deployment, typically at very small scale. A significant gap exists between FL research and clinical application. Current methods fall short of healthcare-grade privacy and real-world constraints, limiting readiness for high-stakes clinical use.

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.