Back to results
US 2024/0195434 A1Published application
Bi-directional compression and privacy for efficient communication in federated learning
AssigneeQUALCOMM Incorporated
InventorsMatthias REISSER, Aleksei TRIASTCYN, Christos LOUIZOS
Life of this patent
Estimating the term…
Abstract
Certain aspects of the present disclosure provide techniques for performing federated learning, including receiving a global model from a federated learning server; determining an updated model based on the global model and local data; and sending the updated model to the federated learning server using relative entropy coding.
Claims31
A computer-implemented method, comprising:
- receiving a global model from a federated learning server;
- determining an updated model based on the global model and local data;
- and
- sending the updated model to the federated learning server using relative entropy coding.
1 more element
A computer-implemented method, comprising:
- sending a global model to a client device;
- determining a random seed;
- receiving an updated model from the client device using relative entropy coding;
- and
- determining an updated global model based on the updated model from the client device.
2 more elements
19-22. (canceled)
A processing system, comprising:
- a memory comprising computer-executable instructions;
- and
- one or more processors configured to execute the computer-executable instructions and cause the processing system to:
- receive a global model from a federated learning server;
- determine an updated model based on the global model and local data;
- and
- send the updated model to the federated learning server using relative entropy coding.
4 more elements
Dependencies are inferred from the claim text.