Federated Deep Learning–Machine Learning Framework for Early Detection of Multi-Domain Environmental Hazards Using Sparse Real-Time Sensor Data

Abstract

Increasingly severe environmental hazards (e.g., air pollution, floods, forest fires, and industrial gas leaks) require intelligent monitoring systems that can work in sparse, distributed, and privacy-sensitive data environments. Because traditional centralized solutions have high communication overhead, latency, and privacy issues, they are not effective for large-scale Internet of Things (IoT) deployments. This paper presents FDL-ML, a novel Federated Deep Learning-Machine Learning framework for early detection of multi-domain environmental hazards through sparse real-time sensor data. The FDL-ML architecture integrates federated learning with hybrid machine learning and deep learning to allow for decentralized model training across distributed IoT sensor nodes without sharing raw data. A robust preprocessing layer consists of techniques such as imputing, filtering, normalizing, and detecting anomalies, which enhance data quality and reliability. Random Forest and Support Vector Machine models are used to analyze structured environmental data, while convolutional neural networks and long short-term memory networks model complex spatial and temporal patterns. Federated averaging is used to combine individual model updates into a global model while maintaining privacy and reducing communication costs. Simulation experiments with 100 distributed sensor nodes and approximately 4.32 million environmental data samples were conducted to evaluate the performance of the proposed framework. The hazard detection accuracy of this framework is 91%, which exceeds the performance of traditional centralized and standalone models. In addition, the proposed framework reduced latency by 35%, communication costs by 40%, and had a 30% increase in energy efficiency compared to traditional centralized monitoring solutions. The proposed FDL-ML System also provides superior scalability; is robust against sparse sensor data; and provides real-time identification of hazards. These results demonstrate the potential of this framework to be a scalable, privacy-preserving, and resource-efficient solution for next-generation environmental monitoring applications in smart cities, industrial settings, and disaster-prone areas.