Physics-Informed Federated Learning for Decentralized Pharmaceutical Crystallization: Achieving Personalized Predictive Accuracy with Minimal Data
Abstract
Abstract In decentralized pharmaceutical manufacturing, developing robust predictive models for crystallization is often hindered by proprietary data silos and the inherent heterogeneity of site-specific process dynamics. Standard machine learning approaches struggle with data scarcity and frequently fail to respect the underlying physical laws, leading to catastrophic divergence in outlier scenarios. This study introduces a novel Physics-Informed Federated Learning (F-PINN) framework designed to enable collaborative model training across multiple manufacturing sites while strictly preserving data privacy. By embedding the Population Balance Equation (PBE) directly into the federated global loss function, the F-PINN framework acts as a "Physical Foundation Model" that remains robust against sensor bias and significant data gaps. We further propose a Personalized Adaptation phase that allows the global model to fine-tune to local growth kinetics (G) and residence times ( τ ). Results demonstrate that while standard federated averaging yields a generalized model, our personalized F-PINN approach achieves a 99.72% reduction in Mean Squared Error (MSE) compared to data-only models in high-heterogeneity sites. Specifically, the physics-informed priority prevents the model from diverging under noisy conditions, maintaining structural stability where standard neural networks fail. This research provides a scalable, privacy-preserving pathway for the implementation of Pharma 4.0…
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