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Abstract

Methods and systems of training neural networks with federated learning. Server-maintained machine learning models are sent from a server to clients, yielding local machine learning models. At each client, the models are trained with local data to determine a respective cross entropy loss and a distillation loss based on foundation models. Respective weights are updated at each client for each of the local machine learning model based on the losses. The updated weights are transferred to the server without transferring the locally-stored data, whereupon they are aggregated and transferred back to the clients. At each client, the local machine learning model is updated with the aggregated updated weights.

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First Claim