Deep learning has been successful in the theoretical aspect. For deep learning to succeed in industry, we need to have algorithms capable of handling many inconsistencies appearing in real data. These inconsistencies can have large effects on the implementation of a deep learning algorithm. Artificial Intelligence is currently changing the medical industry. However, receiving authorization to use medical data for training machine learning algorithms is a huge hurdle. A possible solution is sharing the data without sharing the patient information. We propose a multi-party computation protocol for the deep learning algorithm. The protocol enables to conserve both the privacy and the security of the training data. Three approaches of neural networks assembly are analyzed: transfer learning, average ensemble learning, and series network learning. The results are compared to approaches based on data-sharing in different experiments. We analyze the security issues of the proposed protocol. Although the analysis is based on medical data, the results of multi-party computation of machine learning training are theoretical and can be implemented in multiple research areas.

翻译：深度学习在理论方面已取得显著成功。要使深度学习在工业领域取得成功，我们需要能够处理实际数据中大量不一致性的算法。这些不一致性可能对深度学习算法的实现产生重大影响。人工智能目前正在改变医疗行业。然而，获取使用医疗数据训练机器学习算法的授权是一个巨大障碍。一种可能的解决方案是在不共享患者信息的情况下共享数据。我们提出了一种用于深度学习算法的多方计算协议。该协议能够同时保护训练数据的隐私和安全。本文分析了三种神经网络集成方法：迁移学习、平均集成学习和串行网络学习。在不同实验中，将结果与基于数据共享的方法进行了比较。我们分析了所提出协议的安全性问题。尽管分析基于医疗数据，但机器学习训练多方计算的结果具有理论性，可在多个研究领域实施。