Abstract: Methods and systems for federated learning using feature normalization are disclosed. A client implements a local model including at least: a feature extraction subnetwork to extract a feature vector from input data, a normalization layer to normalize the feature vector, and a final layer to generate a prediction output from the normalized feature vector. The local model is initialized using a set of global parameters received from a central server. The local model is updated using data sampled from a local dataset. Information about a state of the updated local model is transmitted to the central server.

Abstract: Apparatuses, systems, and techniques to train a generative model based at least in part on a private dataset. In at least one embodiment, the generative model is trained based at least in part on a differentially private Sinkhorn algorithm, for example, using backpropagation with gradient descent to determine a gradient of a set of parameters of the generative models and modifying the set of parameters based at least in part on the gradient.

Abstract: Apparatuses, systems, and techniques to train a generative model based at least in part on a private dataset. In at least one embodiment, the generative model is trained based at least in part on a differentially private Sinkhorn algorithm, for example, using backpropagation with gradient descent to determine a gradient of a set of parameters of the generative models and modifying the set of parameters based at least in part on the gradient.