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Federated learning is an attractive framework for the massively distributed training of deep learning models with thousands or even millions of participants [1]. In every round, the central server distributes the current joint model to a random subset of participants. Each of them trains locally and submits an updated model to the server, which averages the updates into the new joint model. Motivating applications include training image classifiers and next-word predictors on users’ smartphones. To take advantage of a wide range of non-i.i.d. training data while ensuring participants’ privacy, federated learning by design has no visibility into participants’ local data…

Chang Lei

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