Federated learning fusion analysis framework for multi-source heterogeneous data in urban emergency management
城市应急管理中多源异构数据的联邦学习融合分析框架

摘要

随着城市规模的不断扩展及其面临的应急管理需求日益复杂，城市应急管理中的多源异构数据融合成为一个亟待解决的问题。本文提出了一种基于联邦学习的多源异构数据融合分析框架，旨在通过分布式学习方式整合来自不同传感器、监控系统及社会媒体等异构数据源的有价值信息，从而实现应急响应与决策的精准化与智能化。框架充分考虑数据隐私保护，采用联邦学习技术，使得各参与方无需交换原始数据即可共同训练模型，提高数据安全性和分析效果。实验表明，该框架能够有效提升应急管理系统的响应效率和准确性，适应快速变化的城市环境。

Abstract

With the continuous expansion of urban areas and the increasing complexity of emergency management needs, multi-source heterogeneous data fusion has become an urgent issue in urban emergency management. This paper proposes a federated learning-based multi-source heterogeneous data fusion framework, aiming to integrate valuable information from diverse data sources including sensors, monitoring systems, and social media through distributed learning methods, thereby achieving precise and intelligent emergency response and decision-making. The framework fully considers data privacy protection by employing federated learning technology, enabling all participants to jointly train models without exchanging raw data, thus enhancing both data security and analytical effectiveness. Experimental results demonstrate that this framework can significantly improve the response efficiency and accuracy of emergency management systems, adapting to rapidly changing urban environments.