The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jan. 13, 2026
Filed:
Mar. 04, 2024
International Business Machines Corporation, Armonk, NY (US);
Rensselaer Polytechnic Institute, Troy, NY (US);
Theodoros Salonidis, Wayne, PA (US);
Yi Zhou, San Jose, CA (US);
Momin Abbas, New York, NY (US);
Parikshit Ram, Atlanta, GA (US);
Nathalie Baracaldo Angel, San Jose, CA (US);
Horst Cornelius Samulowitz, Armonk, NY (US);
Tianyi Chen, Rensselaer, NY (US);
International Business Machines Corporation, Armonk, NY (US);
Rensselaer Polytechnic Institute, Troy, NY (US);
Abstract
An intermediate global lower-level machine learning model is generated by executing federated learning model training using data from an identified first set of lower-level non-Byzantine client computers which are characterized as being non-Byzantine for the lower-level system. A global lower-level machine learning model is generated by executing the federated learning model training using data from an identified second lower-level set of non-Byzantine client computers which are characterized as being non-Byzantine for the lower-level system. An intermediate global upper-level machine learning model is generated by executing the federated learning model training using data from an identified first upper-level set of non-Byzantine client computers which are characterized as being non-Byzantine for an upper-level system. A global upper-level machine learning model is generated by executing the federated learning model training using data from a second upper-level set of client computers which are characterized as being non-Byzantine for the upper-level system.