Differentially Private Federated Learning for High-Accuracy Carbon Footprint Prediction that Protects Sensitive Industrial Data

Life Cycle Impact Assessment (LCIA) often lacks accurate data owing to reluctance in industry to share proprietary production information. Here, we present a privacy-preserving framework that improves carbon footprint prediction using federated learning and differential privacy. Our method maintains data confidentiality while enhancing prediction accuracy and consistency. Experiments on public data show strong performance R2 = 0.96 at epsilon=15, comparable to standard and aggregated data models. This approach enables more reliable Scope 3 emissions assessments, supporting accurate and collaborative LCIA amid growing regulatory demands.