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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.