US 12,386,979 B2
Systems and methods for federated model validation and data verification
Shaltiel Eloul, London (GB); Sean Moran, London (GB); Fanny Silavong, London (GB); Sanket Kamthe, London (GB); and Antonios Georgiadis, London (GB)
Assigned to JPMORGAN CHASE BANK, N.A., New York, NY (US)
Filed by JPMORGAN CHASE BANK, N.A., New York, NY (US)
Filed on Jan. 19, 2023, as Appl. No. 18/156,607.
Claims priority of application No. 20220100050 (GR), filed on Jan. 20, 2022.
Prior Publication US 2023/0229786 A1, Jul. 20, 2023
Int. Cl. G06F 21/00 (2013.01); G06F 21/57 (2013.01); G06N 20/00 (2019.01); H04L 29/06 (2006.01)
CPC G06F 21/577 (2013.01) [G06N 20/00 (2019.01); G06F 2221/033 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A method for federated model validation and data verification, comprising:
receiving, by a local computer program executed by client system, a federated machine learning model from a federated model server;
testing, by the local computer program and using a policy service, the federated machine learning model for vulnerabilities to attacks;
accepting, by the local computer program, the federated machine learning model in response to the federated machine learning model passing the testing;
training, by the local computer program, the federated machine learning model using input data comprising local data and outputting training parameters;
identifying, by the local computer program using the policy service, contamination by comparing the training parameters to the input data using an inversion of gradients of the training parameters to the input data; and
providing, by the local computer program, the training parameters to the federated model server.