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Abstract

Methods and systems of training neural networks with federated learning. A portion of a server-maintained machine learning (ML) model is sent from a server to clients, whereupon local ML models are trained with locally-stored data, including determining cross entropy loss for each local ML model. The updated weights are evaluated on a common data set to obtain activation outputs for each layer of the local ML model, which are transferred to the server whereupon they are permuted to match a dimension of the selected client to obtain a matrix, which is sent to the clients. At each client, the local ML model is permuted based on the matrix to obtain permuted weights which are transferred to the server and aggregated. The aggregated permuted weights are transferred to the clients so that the local ML models are updated with the aggregated permuted weights.

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First Claim