Integrating Sensor-Empowered Federated Learning and Smart Contracts for Automatic Flood Risk Management

Abstract

This paper proposes a distributed computing framework that integrates federated learning (FL) and blockchain-enabled smart contracts for automatic flood response management. FL is deployed to process time series sensor data locally for separate sensor devices by training a machine learning (ML) model and then aggregating the trained model parameters obtained from each sensor device to yield final predictions in terms of rainfall levels. The predicted precipitation levels are input into a predefined smart contract to automatically trigger mitigation strategies to be used by frontline safety and maintenance personnel. The results obtained using the proposed framework demonstrate both improved prediction accuracy and data privacy preservation. The validation effort shows that smart contracts can execute context-aware actions, thus enabling fast decision-making for flood response. The developed framework holds the potential to revolutionize decentralized data management, enhance efficient data processing, and ensure data privacy, transparent and secure data communication, and resilience against centralized failures, thereby enabling a more intelligent infrastructure management system to mitigate flood impacts.