# OpenFL: An open-source framework for Federated Learning

arXiv: https://arxiv.org/abs/2105.06413 · HTML: https://mlsanity.com/paper/2105.06413/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 46 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2006.16067/) (2020)
- [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://mlsanity.com/paper/1512.01274/) (2015)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [The Federated Tumor Segmentation (FeTS) Challenge](https://mlsanity.com/paper/2105.05874/) (2021)
- [The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study](https://mlsanity.com/paper/1911.00515/) (2019)
- [Towards Federated Graph Learning for Collaborative Financial Crimes Detection](https://mlsanity.com/paper/1909.12946/) (2019)
- [GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging](https://mlsanity.com/paper/2103.01006/) (2021)

## Built on by (most cited first, 30 of 58)

- [MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation](https://mlsanity.com/paper/2110.01406/) (2021)
- [Federated Learning Enables Big Data for Rare Cancer Boundary Detection](https://mlsanity.com/paper/2204.10836/) (2022)
- [NVIDIA FLARE: Federated Learning from Simulation to Real-World](https://mlsanity.com/paper/2210.13291/) (2022)
- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [A Survey on Efficient Federated Learning Methods for Foundation Model Training](https://mlsanity.com/paper/2401.04472/) (2024)
- [CrowdGuard: Federated Backdoor Detection in Federated Learning](https://mlsanity.com/paper/2210.07714/) (2022)
- [FS-Real: Towards Real-World Cross-Device Federated Learning](https://mlsanity.com/paper/2303.13363/) (2023)
- [Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework](https://mlsanity.com/paper/2409.11585/) (2024)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [FedCostWAvg: A new averaging for better Federated Learning](https://mlsanity.com/paper/2111.08649/) (2021)
- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion](https://mlsanity.com/paper/2208.07978/) (2022)
- [Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications](https://mlsanity.com/paper/2304.12012/) (2023)
- [Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation](https://mlsanity.com/paper/2406.12815/) (2024)
- [Atlas: A Framework for ML Lifecycle Provenance & Transparency](https://mlsanity.com/paper/2502.19567/) (2025)
- [FedPIDAvg: A PID controller inspired aggregation method for Federated Learning](https://mlsanity.com/paper/2304.12117/) (2023)
- [Flame: Simplifying Topology Extension in Federated Learning](https://mlsanity.com/paper/2305.05118/) (2023)
- [Federated Linear Contextual Bandits with Heterogeneous Clients](https://mlsanity.com/paper/2403.00116/) (2024)
- [FLEX: FLEXible Federated Learning Framework](https://mlsanity.com/paper/2404.06127/) (2024)
- [SecFL: Confidential Federated Learning using TEEs](https://mlsanity.com/paper/2110.00981/) (2021)
- [Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning](https://mlsanity.com/paper/2302.07946/) (2023)
- [Model-Agnostic Federated Learning](https://mlsanity.com/paper/2303.04906/) (2023)
- [Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives](https://mlsanity.com/paper/2307.02140/) (2023)
- [Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems](https://mlsanity.com/paper/2405.05611/) (2024)
- [Federated and Transfer Learning for Cancer Detection Based on Image Analysis](https://mlsanity.com/paper/2405.20126/) (2024)
- [Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning](https://mlsanity.com/paper/2409.16495/) (2024)
- [The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning](https://mlsanity.com/paper/2512.06206/) (2025)
- [Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentation](https://mlsanity.com/paper/2603.04887/) (2026)

## Abstract (arXiv)

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.