Works

Privacy-Preserving Transfer Learning Framework for Building Energy Forecasting with Fully Anonymized Data

A novel framework that enables effective transfer learning using exclusively anonymized time-series data, achieving median MSE reductions of 27–31% across 89 real-world buildings while requiring only 0.51% of federated learning's communication bandwidth.