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Abstract

Methods and systems for training neural networks with federated learning. A portion of a server-maintained machine-learning model is transferred from a server to clients, yielding a plurality of local machine-learning models. At each client, the local models are trained with locally-stored data, including determining a respective cross entropy loss for each local models. Weights are updated for each local model, and evaluated based on a common dataset to obtain activation outputs for each layer. These are transferred to the server without transferring the locally-stored data of the clients, whereupon they are permuted according to the one respective updated weight to match a dimension of the selected client to obtain a matrix, which is sent to each client for permuting the local models based on the matrix. The permuted weights are sent to the server, whereupon they are aggregated and transferred back to the clients for updating of the local models.

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First Claim