Federated Learning Lets Data Stay Distributed

That can be a problem when trying to train models that might benefit from more data, but regulatory issues restrict that data's movements, according to Steve Irvine, co-founder and CEO of integrate.ai. "[For] a lot of industries, like health care, it's prohibited moving the data across jurisdiction, and so some of the most meaningful use cases that you and I would hope could come into the world -- and developers want to bring into the world -- are blocked because the data can't move," Irvine said. This is where federated learning can help. Federated learning allows for the training of AI models by shifting the paradigm to bring the training function to the data, Irvine told The New Stack. "Instead of data having to come to a central location to train the machine learning model, versions of the model gets sent out to the location where the data resides," he explained.