Source: thenewstack.io
Federated Learning Lets Data Stay Distributed
Not all data can be moved — which can make it difficult to train artificial intelligence models, especially in regulated industries like health care. 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 https://www.linkedin.com/in/steveirvine/?originalSubdomain=ca, co-founder and CEO of https://www.integrate.ai/.
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.
Prior to federated learning, a researcher would have literally had to fly to another country to access health data that contained sensitive data, Irvine said.
While training AI on distributed research data is an obvious use case, https://thenewstack.io/hpe-swarms-machine-learning/ include training models on Internet of Things (IoT) data.
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.
Prior to federated learning, a researcher would have literally had to fly to another country to access health data that contained sensitive data, Irvine said.
While training AI on distributed research data is an obvious use case, https://thenewstack.io/hpe-swarms-machine-learning/ include training models on Internet of Things (IoT) data.
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