Deep neural networks are powerful tools for performing classification and predictive analysis of large datasets. However, certain fields face significant challenges in training deep neural networks due to data sensitivity and privacy. For example, in healthcare, sufficiently deep neural architectures may require secure compilation and analysis of patient data across multiple repositories without direct sharing of raw data. Furthermore, training of deep neural networks may require computationally intense data preparation and training, rendering this process difficult for individual data repositories. Although methods exist to train multi-party deep neural networks, these approaches are computationally ineffective and insecure. Therefore, there is a need for methods that enable efficient and secure training of deep neural networks over several data sources.