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Abstract

Methods and systems for training neural networks with federated learning. Respective weights are transferred from each client to a respective neighboring client without transferring the locally-stored data of the clients. All models are permuted according to the respective weights to match the respectively updated weights to obtain permuted weights. The permuted weights are aggregated at the clients. At each client, local machine learning models are trained with locally-stored data, wherein the training includes determining a respective cross entropy loss for each of the plurality of local machine learning models and a loss computed based on a distance of the local MLM to the aggregated permuted weights. Respective weights of each local machine learning models are updated based on the determined cross entropy loss and the loss.

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