Privacy-preserving design of smart products through federated learning

Abstract

The design of smart products calls for new approaches to address the dilemma between effectiveness of machine learning and protection of data privacy. Federated learning is an emerging paradigm of machine learning that enables independent clients to jointly train a global model without breaching data privacy. Federated learning is leveraged to enhance the design of smart products towards privacy-preserving prediction of product states. The formulation, classification, and process of federated learning for product design are presented. An experiment is conducted to validate the effectiveness of federated learning in predicting electricity consumption based on a federation of distributed smart meters.