Architecture-agnostic federated learning system
AssigneeNVIDIA Corporation
InventorsOr Litany, Haggai Maron, David Jesus Acuna Marrero, Jan Kautz, Sanja Fidler, Gal Chechik
Life of this patent
Estimating the term…
Abstract
A method performed by a server is provided. The method comprises sending copies of a set of parameters of a hyper network (HN) to at least one client device, receiving from each client device in the at least one client device, a corresponding set of updated parameters of the HN, and determining a next set of parameters of the HN based on the corresponding sets of updated parameters received from the at least one client device. Each client device generates the corresponding set of updated parameters based on a local model architecture of the client device.
Claims25
A method for training a hyper network (HN), the method comprising:
- sending, by a server to each of a plurality of client devices, a set of parameters of the HN;
- receiving, by the server from each of one or more respective client devices of the plurality of client devices, a respective set of locally updated HN parameters;
- and
- generating, by the server and based on the one or more respective sets of locally updated HN parameters received from the one or more respective client devices, a globally updated set of parameters of the HN,
- wherein each of the one or more respective client devices generates a respective set of locally updated HN parameters by training a respective local copy of the HN based on a respective local neural network architecture.
A method for training a hyper network (HN), the method comprising:
- receiving, by a client device, a set of parameters of a hyper network (HN) from a server;
- generating, by the client device, a set of locally updated HN parameters by training a local copy of the HN based on a local neural network architecture;
- and
- sending, by the client device, the set of locally updated HN parameters to the server,
- wherein the server generates a globally updated set of parameters of the HN based on the set of locally updated HN parameters.
A system for training a hyper network (HN), the system comprising:
- a server;
- and
- a plurality of client devices;
- wherein the server is configured to facilitate:
- sending a set of parameters of the HN to each of the plurality of client devices;
- receiving, from each of one or more respective client devices of the plurality of client devices, a respective set of locally updated HN parameters;
- and
- generating, based on the one or more respective sets of locally updated HN parameters, a globally updated set of parameters of the HN, and
- wherein each respective client device in the at least one of the plurality of client devices is configured to facilitate:
- receiving, from the server, the set of parameters of the HN;
- generating the respective set of locally updated HN parameters based on a local neural network architecture of the respective client device;
- and
- sending, to the server, the respective set of locally updated HN parameters.
Dependencies are inferred from the claim text.