Decentralized water quality classification using federated learning with recurrent neural networks

Abstract

The preservation of human health and the integrity of the ecosystem depend on regular assessments of water quality. Nevertheless, conventional centralised methodologies encounter difficulties concerning data privacy, scalability, and resource constraints. This research presents an innovative, privacy-preserving approach utilising federated learning with recurrent neural networks (RNNs) to overcome these restrictions. Our approach facilitates decentralised training of predictive models across remote water monitoring sources, such as treatment facilities and field stations, without the need to transfer sensitive data, in contrast to previous methods. Our methodology uniquely captures temporal trends in water quality data, such as variations in pH, dissolved oxygen, and pollutant levels, by integrating federated learning with Long Short-Term Memory (LSTM) networks. Experimental results indicate that our method attains a classification accuracy of 92.3% while simultaneously improving data confidentiality and scalability, rendering it a practical instrument for decentralised water quality monitoring in real-world applications. This new approach provides a significant leap in environmental monitoring by enabling secure, large-scale, and collaborative analysis of water quality data.