Generative adversarial network-based imputation and personalized federated learning with Convolutional Neural Network-Long Short-Term Memory for air quality index prediction
Year: 2025
Type: article
Abstract: Air quality forecasting is a critical task for mitigating the adverse effects of pollution on public health and the environment. This research introduces an advanced Generative Adversarial Network based Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) Federated Framework (GC-LFF) that addresses the limitations of traditional centralized machine learning models, which struggle with data variability and privacy concerns. The proposed framework employs advanced convolutional neural networks and long short-term memory based architectures to effectively capture spatial and temporal dependencies in decentralized air quality data. Additionally, a Generative adversarial network-based imputation module is incorporated to manage missing or corrupted sensor data, ensuring completeness without necessitating centralized data sharing. Each participating city in GC-LFF trains its model locally using its air quality data and uses a weighted Federated Averaging technique to aggregate model updates on a central server. The final global model of each node is then customized to increase the accuracy of local forecasting. The GC-LFF framework was evaluated using individualized local data-driven fine-tuning across 26 metropolitan nodes. With a Mean square error (MSE) of 499.16, a Root Mean square error (RMSE) of 19.83, a Mean Absolute Error (MAE) of 14.41, and an R-squared ( R 2 ) of 0.72 after the removal of outliers. The model’s resilience and flexibility were validated using standard deviation analysis and confidence intervals. The results demonstrate its strong performance and scalability for practical implementation.
Authors: Shweta Roy, Nisha Kandhoul, Sanjay K. Dhurandher
Institution: Netaji Subhas University of Technology
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,200
OpenAlex ID: W4411369785