Weiwei Dai

FedEye: A Scalable and Adaptable Federated Learning Platform for AI in Eye Health

The advancements in artificial intelligence (AI) and machine learning (ML) have brought about significant changes in healthcare. The success of machine learning in healthcare depends on the quality and quantity of data used to train models. However, the data are generated and saved at a variety of places, which are restricted to be shared, that limits the development of intelligent models.

Federated learning (FL) has emerged as a solution to train models across multiple institutions without sharing data, and the FL frameworks and platforms play an important role in enabling the implementation and application of machine learning models in such scenarios. In this report, we introduce FedEye, a scalable and adaptable federated learning platform designed for medical professionals with some principles such as low-code programming, accessibility, and separation of concerns. FedEye can make it easier for medical researchers to understand and use it for their own and collaborative research tasks.

Key sentences

• FedEye
• Application of FedEye
• Federated Learning System
• Medical Application
• Healthcare