A Federated Learning Approach to Multimodal Data Privacy for Rapid Disaster Analysis

This research paper introduces a Federated Learning (FL) framework tailored for multimodal data privacy in disaster analysis, seamlessly integrating geospatial data privacy concerns, privacy preservation techniques, and disaster management applications. The framework adeptly addresses the ethical challenges posed by the usage of geospatial data and the delicate balance between open research and individual privacy. The proposed framework takes advantage of FL's decentralized nature to enable secure and privacy-preserving analysis of multimodal data, including textual content from social media and geospatial information from satellites. Employing advanced privacy preservation techniques, including anonymization and encryption, the framework ensures the protection of individual privacy rights while enhancing disaster management applications [6]. The intersection of these components is pivotal, emphasizing the careful equilibrium between data utility and privacy protection. Furthermore, the paper presents compelling results derived from the application of the proposed FL framework, showcasing its effectiveness in disaster analysis. The results highlight the successful integration of decentralized multimodal data, preserving privacy while contributing valuable insights to enhance disaster management strategies. This work underscores the importance of striking a thoughtful balance between data utility and individual privacy in advancing disaster research and response capabilities [7].