Feruza Eshimova’s Article in Scopus

An article by Feruza Eshimova, Associate Professor of the Department of Economics and Engineering Sciences, entitled “Federated Multi-Modal Learning for Cross-Platform Image Computation: A Functional Analysis and Nonlinear Optimization Approach to Privacy Preservation” was published in the Q3-ranked international scientific journal Results in Nonlinear Analysis.

The study explores federated multimodal learning, functional analysis, and nonlinear optimization approaches for image computation while preserving privacy.