Systems and methods for a distributed training framework using uniform class prototypes

Abstract

Embodiments described herein provide systems and methods for federated learning. A central system may store a neural network model which has a body of a number of layers, and a classification layer comprising class prototypes which classifies the latent representations output by the body of the model. The central system may initialize the class prototypes so that they are uniformly distributed in the representation space. The model and class prototypes may be broadcast to a number of client systems, which update the body of the model locally while keeping the class prototypes fixed. The clients may return information to the central system including updated local model parameters, and a local representation of the classes based on the latent representation of items in the local training data. Based on the information from the clients, the neural network model may be updated. This process may be repeated iteratively.