Hierarchical Federated Learning with Hybrid Neural Architectures for Predictive Pollutant Analysis in Advanced Green Analytical Chemistry

We propose a hierarchical federated learning (HFL) framework for predictive pollutant analysis in advanced green analytical chemistry (AGAC), addressing the limitations of centralized approaches in scalability and data privacy. The system integrates localized sub-models with hybrid neural architectures, combining LSTM and attention mechanisms to capture temporal dependencies and feature importance in distributed analytical data, while raw measurements remain decentralized. A global aggregator dynamically adjusts model weights based on validation performance and data heterogeneity, ensuring robust adaptation to diverse environmental conditions. The framework interfaces seamlessly with AGAC infrastructure, processing inputs from analytical instruments into standardized sequences and mapping predictions back to pollutant concentrations through calibration curves. Implemented with PyTorch Federated and edge-cloud deployment, the system employs homomorphic encryption for secure data transmission, prioritizing spectral features critical for organic pollutant detection. Our approach achieves superior accuracy and privacy preservation compared to traditional centralized methods, offering a transformative solution for scalable environmental monitoring. The proposed method demonstrates significant potential for real-world applications, particularly in scenarios requiring distributed data collaboration without compromising analytical integrity.