US 12,229,280 B2
Privacy preserving cooperative learning in untrusted environments
Aolin Ding, Piscataway, NJ (US); and Amin Hassanzadeh, Arlington, VA (US)
Assigned to Accenture Global Solutions Limited, Dublin (IE)
Filed by Accenture Global Solutions Limited, Dublin (IE)
Filed on Mar. 15, 2022, as Appl. No. 17/695,820.
Claims priority of provisional application 63/161,883, filed on Mar. 16, 2021.
Prior Publication US 2022/0300618 A1, Sep. 22, 2022
Int. Cl. G06F 21/60 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01)
CPC G06F 21/60 (2013.01) [G06N 3/045 (2023.01); G06N 3/08 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for cooperative training of machine learning models, the method comprising:
generating, by one or more processors, a first parameter set corresponding to a first partial machine learning (ML) model, a second parameter set corresponding to a second partial ML model, a third parameter set corresponding to a third partial ML model, and a fourth parameter set corresponding to a fourth partial ML model,
wherein the first parameter set and the third parameter set correspond to a first splitting of an initial ML model design, and the second parameter set and the fourth parameter set correspond to a second splitting of the initial ML model design;
initiating, by the one or more processors, transmission of the first parameter set to a first client device and of the second parameter set to a second client device;
modifying, by the one or more processors, the third parameter set based on first output data received from the first client device,
wherein the first output data represents output of a first trained ML model that is based on the first parameter set and trained using first client data;
modifying, by the one or more processors, the fourth parameter set based on second output data received from the second client device,
wherein the second output data represents output of a second trained ML model that is based on the second parameter set and trained using second client data; and
aggregating, by the one or more processors, at least the modified third parameter set and the modified fourth parameter set to create an aggregate parameter set corresponding to an aggregate ML model.