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Hospitals lack an objective and privacy-preserving mechanism to compare operational performance against peer institutions. This limits shared learning around capacity, discharge flow, staffing, and service demand. Traditional benchmarking often depends on centralized data warehouses, voluntary reporting, or retrospective surveys. These approaches can create privacy, competitive, regulatory, and selection-bias concerns that discourage full participation. This article proposes a federated analytics framework for computing aggregate operational benchmarks without moving raw hospital data outside local institutional boundaries. The framework would support medians, percentiles, and risk-adjusted comparative indicators through secure aggregation. The framework combines a local data standardization engine, a secure multi-party computation aggregator, a differential privacy injector, and a participatory dashboard. Together, these components would allow each hospital to compare its position against anonymous peer distributions. The framework could enable hospitals to identify performance gaps in bed occupancy, discharge delays, staffing ratios, and service demand while preserving confidentiality. It would be expected to encourage more honest participation because institutional data sovereignty remains intact. A federated analytics approach offers a practical pathway for collaborative operations improvement across health systems. It aligns benchmarking, privacy protection, and organizational learning within a single governance-aware framework.