FeatureCloud: a privacy by design platform for federated learning in biomedicine and beyond
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas including biomedicine and are driven by the increasing amount of available data. However, patient-derived data is often distributed across different institutions and cannot be shared and pooled due to privacy concerns. Privacy-preserving methods, such as Federated Learning (FL), allow for training ML models on distributed data without sharing sensitive information. Despite this advantage, to date, the FL approach is only beginning to be used in biomedicine because the implementation of FL-based tools is time-consuming, and requires advanced programming skills. To remove this roadblock from the way of FL to biomedicine, we present FeatureCloud (https://featurecloud.ai), an all-in-one platform for the development and application of FL-based tools. FeatureCloud provides the developers with well-documented templates and a testing environment for the creation of FL-based apps. The apps which passed validation become available for the end-users in the FeatureCloud AI Store. The FeatureCloud apps are ready-to-use and require no programming skills, enabling FL for a broad community of biomedical researchers. To illustrate the utility of FeatureCloud, we present three FL-based tools: PARTEA (https://exbio.wzw.tum.de/partea/) for privacy-aware time-to-event analysis, Flimma (https://exbio.wzw.tum.de/flimma/) for federated differential expression analysis, and sPLINK (https://exbio.wzw.tum.de/splink) for secure genome-wide association studies (GWAS). We show that these federated apps produce similar results to their centralized versions applied to pooled data regardless of data distribution across the participants. It makes them a promising alternative to meta-analyses, whose results may be strongly affected by imbalanced target class or covariate distributions across the cohorts.
Joint presentation with Mohammad Bakhtiari
Olga Zolotareva is currently associated junior group leader for the Computational Systems Medicine group at the Institute for Computational Systems Biology (CoSy.Bio) at the University of Hamburg, Germany and research employee in the CLINSPECT-M project; developing federated ML methods for FeatureCloud.eu at the Technical University of Munich, Freising, Germany. She graduated with a PhD from the Bioinformatics/Medical Informatics Department of Bielefeld University and holds a diploma of honors in bioengineering from Lomonosov Moscow State University, Russia.