For distributed machine learning with health data we demonstrate how minimizing distance correlation between raw data and intermediary representations (smashed data) reduces leakage of sensitive raw data patterns during client communications while maintaining model accuracy. Leakage (measured using KL Divergence between input and intermediate representation) is the risk associated with the invertibility from intermediary representations, can prevent resource poor health organizations from using distributed deep learning services. We demonstrate that our method reduces leakage in terms of distance correlation between raw data and communication payloads from an order of 0.95 to 0.19 and from 0.92 to 0.33 during training with image datasets while maintaining a similar classification accuracy.
