18494920. SELECTIVELY SHARED CONDENSED DATA FOR EFFICIENT FEDERATED LEARNING (Capital One Services, LLC)
SELECTIVELY SHARED CONDENSED DATA FOR EFFICIENT FEDERATED LEARNING
Organization Name
Inventor(s)
Michael Davis of Arlington VA US
Galen Rafferty of Mahomet IL US
Samuel Sharpe of Cambridge MA US
Brian Barr of Schenectady NY US
Jeremy Goodsitt of Champaign IL US
Taylor Turner of Richmond VA US
Tyler Farnan of San Diego CA US
SELECTIVELY SHARED CONDENSED DATA FOR EFFICIENT FEDERATED LEARNING
This abstract first appeared for US patent application 18494920 titled 'SELECTIVELY SHARED CONDENSED DATA FOR EFFICIENT FEDERATED LEARNING
Original Abstract Submitted
A method and related system for training a machine learning model using a federated learning structure by selectively sharing condensed data between devices includes operations to obtain datasets from client devices comprising a first client device and a second client device and updating a datasets subset to comprise a first dataset. The method further includes updating the datasets subset to comprise a second dataset based on a result indicating that a feature space distance between the first dataset and the second dataset satisfies a set of criteria and sending, to a third client device, the datasets subset comprising the first dataset and the second dataset. The method further includes obtaining, from the third client device, a set of model parameters that is derived from training based on the datasets subset and updating a server version of a machine learning model based on the set of model parameters.
- Capital One Services, LLC
- Michael Davis of Arlington VA US
- Galen Rafferty of Mahomet IL US
- Samuel Sharpe of Cambridge MA US
- Brian Barr of Schenectady NY US
- Jeremy Goodsitt of Champaign IL US
- Taylor Turner of Richmond VA US
- Kenny Bean of Herndon VA US
- Owen Reinert of Queens NY US
- Tyler Farnan of San Diego CA US
- G06N20/00
- CPC G06N20/00
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