Minisymposium Presentation

Federated learning (FL) is a collaborative learning approach where multiple data owners train a model together under the orchestration of a central server by sharing the model trained on their local datasets instead of sharing the data directly. FL enables creation of more robust models without the exposure of local datasets. However, FL by itself, does not guarantee the privacy of data, because the information extracted from the communication of FL algorithms can be accumulated and utilized to infer the private local data used for training. We developed Advanced Privacy Preserving Federated Learning framework (APPFL), with advances in differential privacy, to enable Privacy-Preserving Federated Learning (PPFL). We enabled PPFL through scaled, distributed training on supercomputing resources across multiple institutions to help create robust, trust-worthy AI models in biomedicine and smart grid applications. Setting up a secure high-performance computing FL experiment requires capabilities that may not be available for all. To lower the barrier for leveraging PPFL, we created the Advanced Privacy-Preserving Federated Learning as a service (APPFLx). APPFLx enables cross-silo PPFL with easy to use web interface for managing, deploying, analyzing, and visualizing PPFL experiments. In this talk, we will describe APPFLx and its adoption to biomedical use cases.