Privacy preserving pregnancy weight gain management: demo abstract

Early gestational weight gain prediction can help expecting women overcome several associated risks. However, training the model requires access to centrally stored privacy sensitive weight and other meta-data. In this demo, we present a privacy preserving federated learning approach where we train a global weight gain prediction model by aggregating client models trained locally on their personal data. We showcase a software data-exploration tool that exhibits local model generation, sharing and updating across users and server for proposed collaborative learning. Our proposed model predicts the final weight category with 61.3% accuracy on day 140, with a 8.8% compromise on the centralized training accuracy.