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OpenFL: An open-source framework for Federated Learning

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arxiv 2105.06413 v1 pith:UZBNN6VG submitted 2021-05-13 cs.LG cs.DC

G Anthony Reina , Alexey Gruzdev , Patrick Foley , Olga Perepelkina , Mansi Sharma , Igor Davidyuk , Ilya Trushkin , Maksim Radionov
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Aleksandr Mokrov Dmitry Agapov Jason Martin Brandon Edwards Micah J. Sheller Sarthak Pati Prakash Narayana Moorthy Shih-han Wang Prashant Shah Spyridon Bakas
This is my paper · ORCID
classification cs.LGcs.DC
keywords learningopenflfederatedframeworktrainingcomputationaldatafirst
verification ladder T0 review T1 audit T2 compute T3 formal

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Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (OpenFL https://github.com/intel/openfl) is an open-source framework for training ML algorithms using the data-private collaborative learning paradigm of FL. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and deep learning frameworks. Here, we summarize the motivation and development characteristics of OpenFL, with the intention of facilitating its application to existing ML model training in a production environment. Finally, we describe the first use of the OpenFL framework to train consensus ML models in a consortium of international healthcare organizations, as well as how it facilitates the first computational competition on FL.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 58 citations worldwide. Full citation record

  1. Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub

    cs.SE 2026-07 conditional novelty 6.0 of 10

    Federated-learning developers' biggest public pain points are environment setup, API/version breakage, non-IID training instability, and evaluation/privacy integration, with Stack Overflow skewing How and GitHub skewing Why.

  2. A Federated Random Forest Solution for Secure Distributed Machine Learning

    cs.LG 2025-05 conditional novelty 4.0 of 10

    A PySyft-backed federated Random Forest with weighted tree sampling keeps predictive accuracy within 9% of centralized training on two healthcare datasets.

  3. Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation

    cs.CV 2025-02 conditional novelty 4.0 of 10

    Weighting the fine-tuning loss by a pixel-level transfer risk map improves brain MRI segmentation Dice by 1.8% to 4.4% over vanilla fine-tuning.

  4. Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation

    cs.LG 2024-12 reject novelty 4.0 of 10

    A federated learning method for brain tumor segmentation using NNMF-based client selection and HSimAgg aggregation reports Dice of 0.73/0.74/0.82 on external data, but lacks baseline comparisons.

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