Аннотация
Bangladesh is highly vulnerable to floods, with around 70 million people living in flood prone areas and a large share of the population exposed to high flood risk. Disaster response increasingly depends on detailed household data for forecasting, evacuation, and aid distribution. When this data is collected and stored centrally, it exposes vulnerable groups, especially women, children, and displaced families, to theft, harassment, trafficking, and gender based violence through misuse or data breaches. We review federated learning applications in disaster management and propose a simple conceptual framework for privacy preserving post flood security and resource planning in Bangladesh. The framework model is structured into 04 layers, namely, local data collection, federated training, secure aggregation, and personalized deployment. A structured review of 45 studies covering federated learning algorithms, privacy mechanisms such as secure aggregation and differential privacy, and disaster related applications shows that existing work performs well technically in flood forecasting, disaster detection, and infrastructure monitoring while keeping data local. However, most studies still focus on accuracy and efficiency without addressing human security risks during aid operations. The suggested conceptual framework combines the ideas of federated optimization, Byzantine robust aggregation, fairness systems, personalized learning, and multi stakeholder governance to safeguard the vulnerable groups in post flood response. It considers system heterogeneity, the communication constraints, and the organizational capacity variation. The framework extends evaluation criteria to include human-centered indicators such as reduction of security incidents and fair distribution of aid. This work outlines future directions for deploying federated learning in disaster settings where beneficiary data collection creates direct protection risks.