Federated Learning (FL) is a machine learning approach that allows for the training of a model across multiple decentralized devices or institutions, without the need to centralize the data on a single server. It has been used across several industries, from mobile device keyboards to autonomous vehicles to oil rigs. It is particularly useful in the healthcare industry, where sensitive patient data is involved and strict regulations need to be followed to protect the privacy of individuals. In this blog post, we will discuss some practical steps to implementing a federated learning project with healthcare data. First, it is important to understand the requirements and constraints of your project.

BOSTON, MA / ACCESSWIRE / August 2, 2022 / Rhino Health, a distributed computer platform leveraging the privacy-preserving concept of federated machine learning, announced that it has joined the Alliance for Artificial Intelligence in Healthcare (AAIH). Founded in 2018, the AAIH is the top global advocacy organization dedicated to the responsible adoption and application of AI/ML technologies in healthcare. It contains over 40 organizations dedicated to this mission, with stakeholders from industry, government, academia, and finance. "For AI to have strong, equitable, replicable, and continuously improving performance in healthcare, access to data is key," said Ittai Dayan, MD, co-founder and CEO of Rhino Health. "Federated data management, computation, and learning provides healthcare access without risking patient privacy. We look forward to contributing our knowledge of distributed computation and federated learning to this esteemed group of organizations to collaboratively advance AI in healthcare."