Fedflow: cloud orchestration for federated learning with the FeatureCloud platform

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ID: 327686
2026
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Ranked #67 of 173 articles by views in Bioinformatics advances

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Abstract
Abstract Motivation Federated learning (FL) enables collaborative model training on geographically distributed genomic and clinical datasets while complying with data privacy laws and regulatory constraints. FeatureCloud is an existing platform for FL that provides an accessible web-based interface and a large repository of implemented methods. However, due to its graphical interface, FeatureCloud requires manual interaction of all participants, limiting automation, iteration, and reproducibility. Results We introduce fedflow, a Python-based command-line tool for headless orchestration of FL tasks with FeatureCloud. This tool uses distributed computing resources such as virtual machines or cloud instances to automate such workflows. This allows for scalable federated computing either in local simulations or deployed in a trusted environment. Further, we demonstrate how fedflow can be used to integrate FeatureCloud in reproducible Snakemake workflows. For this, we reanalyse a metagenomic dataset with two federated algorithms and compare the results to the centralized approach with pooled data. Overall, fedflow enables automation of multi-client FL tasks, facilitates embedding of FeatureCloud in standard bioinformatics pipelines and thereby helps increase reproducibility. Availability Fedflow is open-source and available at https://github.com/W-L/fedflow.
Reference Key
openalex_W7208791777 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lukas Weilguny, Niklas Probul, Yani Ren, Nicolas Pons, Jan Baumbach, Mathieu Almeida
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag255
URL
https://doi.org/10.1093/bioadv/vbag255
Keywords Keywords not found

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