Jpmorgan Chase Bank, N.a.

Varun Babbar

Sean Moran

Fanny Silavong

Rob Otter

Antonios Georgiadis

Systems and methods for noise agnostic federated learning are disclosed. A method may include a client computer program executed by an electronic device in a federated learning computer network comprising a plurality of clients: receiving, from a federated learning computer program, a data format having desirable noise characteristics; transforming a client data set comprising variable noise characteristics to the data format using a client generative adversarial network (GAN); generating client weights for the transformed client data set, wherein the client weights indicate features of the client data set; communicating the client weights to the federated learning computer program; receiving, from the federated learning computer program, adjusted weights, wherein the adjusted weights are based on the client weights and a plurality client weights received from the clients in the federated learning computer network; and updating the client weights for a client machine learning model using the adjusted weights.

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Cambridge, United Kingdom

Varun Babbar