This inventor holds 1 USPTO granted patent. Top assignee: International Business Machines Corporation. Active years: 2026.
Company Filing History:
Years Active: 2026
Title: Annie Abay: Innovator in Federated Learning
Introduction
Annie Abay is a prominent inventor based in San Jose, California. She has made significant contributions to the field of machine learning, particularly through her innovative patent in federated learning. Her work exemplifies the intersection of technology and collaboration, paving the way for advancements in data privacy and machine learning efficiency.
Latest Patents
Annie Abay holds a patent for "Tokenized Federated Learning." This invention provides a method for federated learning (FL) that involves training a machine learning (ML) model collaboratively by initiating a round of FL across various data parties. Each data party is allocated tokens to utilize during the training process. The method includes maintaining a data usage profile for each data party, which indicates the amount of data consumed during training, as well as a participation profile that reflects the data provided by each party. Additionally, the invention selectively allocates new tokens based on these profiles and reimburses tokens utilized during training based on the accuracy measurements of the ML model. This innovative approach enhances the efficiency and effectiveness of federated learning.
Career Highlights
Annie Abay is currently associated with International Business Machines Corporation (IBM), where she continues to push the boundaries of technology and innovation. Her work at IBM has allowed her to collaborate with other talented professionals in the field, contributing to groundbreaking advancements in machine learning.
Collaborations
Annie has worked alongside notable colleagues such as Ali Anwar and Syed Amer Zawad. Their collaborative efforts have fostered an environment of innovation and creativity, leading to significant advancements in their respective fields.
Conclusion
Annie Abay's contributions to federated learning and her innovative patent demonstrate her commitment to advancing technology in a collaborative manner. Her work not only enhances machine learning but also emphasizes the importance of data privacy and efficiency in today's digital landscape.