MATHEMATICS SEMINAR by Gül İnan

In today’s data-driven world, the integration of data from diverse sources is important for improving predictive performance and revealing deeper insights. However, privacy concerns often prevent the sharing of data sources. Moreover, the heterogeneity among data sources, arising from differences in data collection techniques, further complicates the merging process. In this study, motivated by one of the existing studies on clustered collaborative learning approach in the literature, we propose an algorithmic recipe that employs clustering and collaboration among data sources without sharing raw data.