A Comparison of Methods for Neural Network Aggregation
This work addresses privacy concerns in medical AI by enabling secure model training without sharing patient data, though it is incremental as it builds on existing multi-party computation techniques.
The paper tackles the challenge of training deep learning models on sensitive medical data by proposing a multi-party computation protocol that preserves privacy and security, comparing three neural network aggregation methods (transfer learning, average ensemble, and series network) against data-sharing approaches.
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.