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Artificial neural network has achieved unprecedented success in the medicaldomain. This success depends on the availability of massive and representativedatasets. However, data collection is often prevented by privacy concerns andpeople want to take control over their sensitive information during bothtraining and using processes. To address this problem, we propose aprivacy-preserving method for the distributed system, Stochastic Channel-BasedFederated Learning (SCBF), which enables the participants to train ahigh-performance model cooperatively without sharing their inputs.Specifically, we design, implement and evaluate a channel-based updatealgorithm for the central server in a distributed system, which selects thechannels with regard to the most active features in a training loop and uploadsthem as learned information from local datasets. A pruning process is appliedto the algorithm based on the validation set, which serves as a modelaccelerator. In the experiment, our model presents better performances andhigher saturating speed than the Federated Averaging method which reveals allthe parameters of local models to the server when updating. We also demonstratethat the saturating rate of performance could be promoted by introducing apruning process. And further improvement could be achieved by tuning thepruning rate. Our experiment shows that 57% of the time is saved by the pruningprocess with only a reduction of 0.0047 in AUCROC performance and a reductionof 0.0068 in AUCPR.

Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving