Federated Learning for Healthcare
Date:
The aim of this tutorial is to facilitate education on how to perform Federated Learning on both simulated and real world studies from software-based privacy-preserving techniques (e.g. DP), to hardware-based trusted execution environments (TEEs).
Tutorial structure focuses on specific clearly indicated parts for beginners and for more advanced attendees. Data scientists of different medical imaging communities (e.g., radiology, pathology) are considered during this tutorial on the opportunities and challenges in developing and using FL fortraining Al models acrossinstitutions using privacy preserving techniques. We plan on covering a spectrum of techniques, from software-based approaches that can be considered a method or a metric (e.g., differential privacy), to hardware-based trusted execution computing environments (TEEs).
The motivation for the tutorial is driven by the need to train and validate deep learning models across data silos, to create models that gain knowledge from diverse patient populations and hence generalize well, mitigate bias, and pave the way towards addressing health disparities.