Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving
This addresses privacy concerns in medical data sharing for federated learning, though it appears incremental as it builds on existing federated learning methods with channel-based updates and pruning.
The paper tackles the problem of privacy-preserving federated learning for medical data by proposing Stochastic Channel-Based Federated Learning (SCBF), which selects and uploads only active channels to avoid sharing raw inputs, resulting in better performance and faster convergence than Federated Averaging while saving 57% of time with minimal accuracy loss.
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. To address this problem, we propose a privacy-preserving method for the distributed system, Stochastic Channel-Based Federated Learning (SCBF), which enables the participants to train a high-performance model cooperatively without sharing their inputs. Specifically, we design, implement and evaluate a channel-based update algorithm for the central server in a distributed system, which selects the channels with regard to the most active features in a training loop and uploads them as learned information from local datasets. A pruning process is applied to the algorithm based on the validation set, which serves as a model accelerator. 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 when updating. 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. 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.