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Abstract

Methods and systems of training neural networks with federated learning. Machine learning models are sent from a server to clients, yielding local machine learning models. At each client, the models are trained with locally-stored data, including determining a respective cross entropy loss for each of the plurality of local machine learning models. Weights for each local model are updated, and transferred to the server without transferring locally-stored data. The transferred weights are aggregated at the server to obtain an aggregated server-maintained machine learning model. At the server, a distillation loss based on a foundation model is generated. The aggregated server-maintained machine learning is updated to obtain aggregated respective weights, which are transferred to the clients for updating in the local models.

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First Claim