Federated (de-centralized) learning (FL) is an approach that downloads the current model and computes an updated model at the device itself using local data, rather than going to one pool to update the device. These locally trained models are then sent from the devices back to the central server where they are aggregated and then a single consolidated and improved global model is sent back to the devices. Federated learning makes it possible for AI algorithms to gain experience from a vast range of data located at different sites. Read More

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