Federated Learning Framework for Distributed Calibration of Electrochemical Sensor Networks: A Novel Approach to Analytical Quality Assurance

The issues of calibration accuracy, sensor drift compensation, and rigorous quality assurance of analytical results are particularly challenging in the deployment of electrochemical sensor networks at scale without compromising data privacy. In this manuscript, we propose a Data Quality-aware Federated Learning (DQFed) framework designed explicitly for electrochemical sensor calibration. The architecture preserves the locality of raw data while enabling cooperative model training across heterogeneous sensor nodes. Using a synergistic approach, we combine physics-based constraints with quality-based aggregation in a rigorous manner that addresses challenges in electrochemical sensing, including temporal drift, cross-sensitivity, and inter-unit variability specific to this modality. In the scheme, adaptive weighting is incorporated to address heterogeneity in sensor data quality and changes in data quality, ensuring that the global model converges well even when some local data is poorly observed. We show that, through large-scale simulations, our framework achieves a mean absolute error (MAE) of 14.0 ppb, representing a 23.7% improvement over centralized machine learning and a 41.3% improvement over local-only calibration, and that it reduces communication overhead by 67% relative to purely local calibration schemes. The drift-compensation module maintains calibration accuracy throughout a 90-day deployment, increasing it by 50.1%. By systemically reviewing the state-of-the-art techniques of calibration methods and federated learning architectures, we can identify the critical nexus points in the existing solutions and present customized solutions, which are feasible for practical applications.