Federated learning framework integrating REFINED CNN and Deep Regression Forests

Daniel Nolte; Omid Bazgir; Souparno Ghosh; Ranadip Pal

doi:10.1093/bioadv/vbad036

Federated learning framework integrating REFINED CNN and Deep Regression Forests

Bioinform Adv. 2023 Mar 22;3(1):vbad036. doi: 10.1093/bioadv/vbad036. eCollection 2023.

Authors

Daniel Nolte¹, Omid Bazgir², Souparno Ghosh³, Ranadip Pal¹

Affiliations

¹ Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
² Genentech, South San Francisco, CA 94080, USA.
³ Department of Statistics, University of Nebraska-Lincoln, Lincoln, NB 68588, USA.

Abstract

Summary: Predictive learning from medical data incurs additional challenge due to concerns over privacy and security of personal data. Federated learning, intentionally structured to preserve high level of privacy, is emerging to be an attractive way to generate cross-silo predictions in medical scenarios. However, the impact of severe population-level heterogeneity on federated learners is not well explored. In this article, we propose a methodology to detect presence of population heterogeneity in federated settings and propose a solution to handle such heterogeneity by developing a federated version of Deep Regression Forests. Additionally, we demonstrate that the recently conceptualized REpresentation of Features as Images with NEighborhood Dependencies CNN framework can be combined with the proposed Federated Deep Regression Forests to provide improved performance as compared to existing approaches.

Availability and implementation: The Python source code for reproducing the main results are available on GitHub: https://github.com/DanielNolte/FederatedDeepRegressionForests.

Contact: ranadip.pal@ttu.edu.

Supplementary information: Supplementary data are available at Bioinformatics Advances online.