US 2024/0403655 A1Published application

Federated learning simulator for flexible local and global training

AssigneeDell Products L.P.

InventorsPaulo Abelha Ferreira, Pablo Nascimento da Silva, Isabella Costa Maia, Iam Palatnik de Sousa

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Abstract

One example method includes, for each federated learning simulation, defining a machine learning model that is used in the federated learning simulation. The machine learning model has associated variables and is implemented at edge nodes and a central node of the federated learning simulation. A first variable list is defined that specifies associated variables that are to be optimized at the edge nodes of the federated learning simulation. A second variable list is defined that specifies associated variables that are to be provided by the edge nodes to the central node of the federated learning simulation. The associated variables included in the first variable list are optimized at the edge nodes of federated learning simulation. The associated variables that are included in the second variable list and that are provided by the edge nodes of the federated learning simulation are aggregated by the central node of the federated learning simulation.

Claims20

Dependent claims

A method, comprising:

for each federated learning simulation of a plurality of federated learning simulations:
defining a machine learning model that is to be used in the federated learning simulation, the defined machine learning model having one or more associated variables, the defined machine learning model being implemented at one or more edge nodes of the federated learning simulation and at a central node of the federated learning simulation;
defining a first variable list that specifies one or more of the associated variables that are to be optimized at the one or more edge nodes of the federated learning simulation;
defining a second variable list that specifies one or more of the associated variables that are to be provided by the one or more edge nodes of the federated learning simulation to the central node of the federated learning simulation;
optimizing the one or more associated variables included in the first variable list at the one or more edge nodes of the federated learning simulation;
and
aggregating at the central node of the federated learning simulation the one or more associated variables that are included in the second variable list and that are provided to the central node of the federated learning simulation by the one or more edge nodes of the federated learning simulation.

4 more elements

A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:

for each federated learning simulation of a plurality of federated learning simulations:
defining a machine learning model that is to be used in the federated learning simulation, the defined machine learning model having one or more associated variables, the defined machine learning model being implemented at one or more edge nodes of the federated learning simulation and at a central node of the federated learning simulation;
defining a first variable list that specifies one or more of the associated variables that are to be optimized at the one or more edge nodes of the federated learning simulation;
defining a second variable list that specifies one or more of the associated variables that are to be provided by the one or more edge nodes of the federated learning simulation to the central node of the federated learning simulation;
optimizing the one or more associated variables included in the first variable list at the one or more edge nodes of the federated learning simulation;
and
aggregating at the central node of the federated learning simulation the one or more associated variables that are included in the second variable list and that are provided to the central node of the federated learning simulation by the one or more edge nodes of the federated learning simulation.

4 more elements

Dependencies are inferred from the claim text.