JMIR Publications

ABSTRACT

Background:

Artificial neural network has achieved unprecedented success in the medical domain. This success depends on the availability of massive and representative datasets. However, data collection is often prevented by privacy concerns and people want to take control over their sensitive information during both training and using processes.

Objective:

To address the security and privacy issues, we propose a privacy-preserving method for the analysis of distributed medical data. The proposed method, Stochastic Channel-Based Federated Learning (SCBF), enables the participants to train a high-performance model cooperatively and in a distributed manner without sharing their inputs.

Methods:

Specifically, we design, implement and evaluate a channel-based update algorithm for the central server in a distributed system. The update algorithm will select the channels with regard to the most active features in a training loop and upload them as learned information from local datasets. A pruning process, which serves as a model accelerator, is applied to the algorithm based on the validation set.

Results:

We construct a distributed system consisting of 5 clients and 1 server. Our trials show that the Stochastic Channel-Based Federated Learning method can achieve an AUCROC of 0.9776 and an AUCPR of 0.9695 with 10% channels shared with the server. Compared with Federated Averaging algorithm, the proposed method achieves 0.05388 higher in AUCROC and 0.09695 higher in AUCPR. In addition, our experiment shows that 57% of the time is saved by the pruning process with only a reduction of 0.0047 in AUCROC performance and a reduction of 0.0068 in AUCPR.

Conclusions:

In the experiment, our model presents better performances and higher saturating speed than the Federated Averaging method, which reveals all the parameters of local models to the server. We also demonstrate that the saturating rate of performance could be promoted by introducing a pruning process and further improvement could be achieved by tuning the pruning rate.