Federated Learning for Medical AI

Unlike conventional machine learning which works by bringing data to the code, Federated Learning brings code to the data. In healthcare where the available data is never enough, Federated Learning can do wonders by enabling AI models to learn on private data without compromising privacy.

Conventional machine learning works on the paradigm of bringing the data to the code. The data used to train the models is centralized in a single storage either on a local disk or on the cloud at a data center. Data samples are loaded from this centralized store while training the model.

Many valuable datasets however are highly decentralized in nature. Take, for instance, patient data lying with hospitals. The data in custody of every hospital forms a data island, and each such data island has characteristics that it does not share with data islands from other hospitals. Hence, a model trained on data from one hospital might show poor generalization on data from another hospital. The conventional approach of pooling data from all hospitals and building a single model on it presents several challenges. To begin with, hospitals are uncomfortable sharing patient data, and governments…