Federated Learning with Privacy-Preserving Ensemble Attention Distillation
This addresses privacy concerns in federated learning for clinical applications where patient data cannot be transferred, though it appears incremental as it builds on existing FL methods.
The paper tackles the privacy leakage risk in federated learning by proposing a framework that uses unlabeled public data for one-way offline knowledge distillation, achieving competitive performance on image classification, segmentation, and reconstruction tasks.
Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. This is particularly relevant for clinical applications since patient data are usually not allowed to be transferred out of medical facilities, leading to the need for FL. Existing FL methods typically share model parameters or employ co-distillation to address the issue of unbalanced data distribution. However, they also require numerous rounds of synchronized communication and, more importantly, suffer from a privacy leakage risk. We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation in this work. The central model is learned from local knowledge via ensemble attention distillation. Our technique uses decentralized and heterogeneous local data like existing FL approaches, but more importantly, it significantly reduces the risk of privacy leakage. We demonstrate that our method achieves very competitive performance with more robust privacy preservation based on extensive experiments on image classification, segmentation, and reconstruction tasks.