This blog post is co-written with Chaoyang He and Salman Avestimehr from FedML.
Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at any single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a centralized data repository. Because they are in a highly regulated domain, HCLS partners and customers seek privacy-preserving mechanisms to manage and analyze large-scale, distributed, and sensitive data.
To mitigate these challenges, we propose using an open-source federated learning (FL) framework called FedML, which enables you to analyze sensitive HCLS data by training a global machine learning model from distributed data held locally at different sites. FL doesn’t require moving or sharing data across sites or with a centralized server during the model training process.
In this two-part series, we demonstrate how you can deploy a

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