Abstract:In response to various privacy risks, researchers and practitioners have been exploring different paradigms that can leverage the
increased computational capabilities of consumer devices to train machine (ML) learning models in a distributed fashion without
requiring the uploading of the training data from individual devices to central facilities. For this purpose, federated learning (FL)
was proposed as a technique that can learn a global machine model at a central master node by the aggregation of models tra… Show more
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