US 12,333,400 B2
Systems and methods for federated learning using distributed messaging with entitlements for anonymous computation and secure delivery of model
Monik Raj Behera, Odisha (IN); Sudhir Upadhyay, Edison, NJ (US); Rob Otter, Witham (GB); and Suresh Shetty, Mangalore (IN)
Assigned to JPMORGAN CHASE BANK, N.A., New York, NY (US)
Filed by JPMORGAN CHASE BANK, N.A., New York, NY (US)
Filed on Nov. 22, 2021, as Appl. No. 17/456,113.
Claims priority of application No. 202011050561 (IN), filed on Nov. 20, 2020.
Prior Publication US 2022/0164712 A1, May 26, 2022
Int. Cl. G06N 20/20 (2019.01); G06F 21/60 (2013.01); H04L 9/08 (2006.01); H04L 9/40 (2022.01)
CPC G06N 20/20 (2019.01) [G06F 21/602 (2013.01); H04L 9/0825 (2013.01); H04L 63/04 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A method for federated learning using distributed messaging, comprising:
generating, by an aggregator node in a distributed computer network, an aggregator node public/private key pair;
communicating, by the aggregator node, the aggregator node public key to a plurality of participant nodes in the distributed computer network;
receiving, by the aggregator node and from each of the participant nodes, a message comprising a local machine learning (ML) model encrypted with a participant node private key and with the aggregator node public key, and a participant node public key corresponding to the participant node private key, the participant node public key encrypted with the aggregator node public key;
decrypting, by the aggregator node, the local ML models and the participant node public keys using the aggregator node public key;
decrypting, by the aggregator node, the local ML models using the participant node public keys;
generating, by the aggregator node, an aggregated ML model based on the local ML models;
encrypting, by the aggregator node and with each participant node public key, the aggregated ML model; and
communicating, by the aggregator node, the encrypted ML models to all participant nodes;
wherein each participant node decrypts one of the encrypted ML models using its participant node private key, and modifies its local ML model with the aggregated ML model.