| Abstract Scope |
This work presents a federated learning framework that enables B2B collaboration by aligning machine learning models trained on diverse datasets without the need to merge sensitive data. Using a shared calibration set, the framework standardizes uncertainties across models predicting critical material properties, such as strength, toughness, and hardness. It selects the prediction with the highest confidence for each output, allowing businesses to leverage the strengths of all models simultaneously, even when models have differing inputs and outputs. This approach accelerates innovation by enabling faster, more accurate predictions while eliminating the need for data centralization, reducing costs, and shortening time-to-market. Effective uncertainty management minimizes risks and improves decision-making, crucial in industries where safety and performance are paramount. With robust privacy protections, companies across sectors like automotive, aerospace, and manufacturing can confidently adopt decentralized models, optimize processes, gain a competitive edge, and drive innovation without compromising data security. |