Google Inc.

Aditya Krishna Menon

Ankit Singh Rawat

Sanjiv Kumar

Sashank Jakkam Reddi

Generally, the present disclosure is directed to systems and methods that perform spreadout regularization to enable learning of a multi-class classification model in the federated setting, where each user has access to the positive data associated with only a limited number of classes (e.g., a single class). Examples of such settings include decentralized training of face recognition models or speaker identification models, where in addition to the user specific facial images and voice samples, the class embeddings for the users also constitute sensitive information that cannot be shared with other users.

Federated learning with only positive labels

A method for training a machine learning model, e.g., a neural network, using a regularization scheme is disclosed. The method includes generating regularized partial gradients of losses computed using an objective function for training the machine learning model.

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New York, NY, United States of America

Aditya Krishna Menon