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Pandemic surges can rapidly overwhelm hospital capacity, where shortages of beds and nurse fatigue contribute directly to increased excess mortality, making coordinated decision-making across emergency departments, intensive care units, and general wards essential yet difficult to achieve under centralized control systems. Centralized approaches to bed allocation and nurse staffing optimization are limited because each hospital unit holds critical local information—such as real-time patient acuity, staff availability, and infection control status—that cannot be easily shared due to privacy constraints and communication delays during crisis conditions. To address these challenges, we propose a federated multi-agent reinforcement learning framework that enables coordinated decision-making for bed distribution and nurse staffing across hospital units without requiring centralization of sensitive clinical or workforce data. The system consists of local reinforcement learning agents deployed in each unit that participate in federated aggregation, a coordination mechanism that aligns inter-unit policies, and a surge detection module that dynamically switches operational strategies during pandemic escalation periods. This distributed architecture maintains data privacy while supporting adaptive, system-wide coordination under surge conditions, overcoming the limitations of both centralized optimization models and rule-based heuristic approaches.