A Federated AI Framework for Multi-Agency Disaster Coordination in the United States
DOI:
https://doi.org/10.21590/Keywords:
Federated Learning, Disaster Management, Multi-Agency Coordination, Emergency Response, Distributed Artificial Intelligence, Data Privacy.Abstract
Effective disaster response in the United States depends on seamless coordination among multiple agencies operating
across federal, state, and local levels. However, existing coordination systems remain constrained by fragmented data
silos, delayed information exchange, and growing concerns over data privacy and security. These limitations hinder timely
decision-making and reduce the overall efficiency of emergency response operations. This study proposes a federated
artificial intelligence framework designed to enable real-time, privacy-preserving collaboration among diverse disaster
management stakeholders. The framework leverages decentralized machine learning, allowing individual agencies to train
local models on sensitive data while sharing only model updates through a secure aggregation mechanism.
The proposed architecture integrates distributed data sources, edge-based intelligence, and a centralized coordination
layer that supports adaptive decision-making without requiring raw data exchange. A simulation-based evaluation is
conducted using multi-agency disaster scenarios, incorporating heterogeneous data streams such as social media inputs
and operational logs. The results demonstrate that the federated approach significantly improves coordination efficiency,
reduces response latency, and enhances predictive accuracy compared to traditional centralized systems. Additionally,
the framework minimizes communication overhead while maintaining robust data privacy standards, making it suitable
for large-scale deployment.
This research contributes to the advancement of intelligent disaster management systems by presenting a scalable and
interoperable solution that aligns with the complex structure of U.S. emergency response networks. The findings highlight
the potential of federated AI to transform multi-agency coordination, offering a resilient foundation for future smart
disaster response infrastructures.
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