Nicola Rieke, Federated Learning for Healthcare
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Nicola Rieke is a senior solution architect at NVIDIA for deep learning in healthcare. With expertise in the field of medical image processing, applied machine learning and computer-aided medical procedures. In particular, she investigates real-time machine learning approaches for computer-assisted surgical interventions and federated learning for digital health:
Federated learning (FL) is a type of machine learning that allows for distributed data sets to be used collaboratively to create a model without having to send the data to a central location. It can be used in a variety of ways, such as a server-client approach, a peer-to-peer approach, or a hierarchical approach.
For healthcare, FL can be used to enable precision medicine at a large scale, and can also help research for rare diseases
Federated learning allows for the aggregation of data from multiple sources without the need to share the data itself, and can be done in either a synchronous or asynchronous manner. And has been used in a variety of ways in the medical field, such as for electronic health records, predicting hospitalization due to cardiac events, and whole brain segmentation and tumor segmentation in MRI.
When explaining FL to non-technical people, it is important to emphasize that the data remains locally and that what is shared is statistical information