# Federated Learning for Breast Density Classification: A Real-World Implementation

arXiv: https://arxiv.org/abs/2009.01871 · HTML: https://mlsanity.com/paper/2009.01871/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 30 of 62)

- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
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- [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)
- [HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images](https://mlsanity.com/paper/2112.10775/) (2021)
- [The Federated Tumor Segmentation (FeTS) Challenge](https://mlsanity.com/paper/2105.05874/) (2021)
- [Do Gradient Inversion Attacks Make Federated Learning Unsafe?](https://mlsanity.com/paper/2202.06924/) (2022)
- [Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching](https://mlsanity.com/paper/2106.08600/) (2021)
- [Specificity-Preserving Federated Learning for MR Image Reconstruction](https://mlsanity.com/paper/2112.05752/) (2021)
- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [Federated Learning of Generative Image Priors for MRI Reconstruction](https://mlsanity.com/paper/2202.04175/) (2022)
- [CrowdGuard: Federated Backdoor Detection in Federated Learning](https://mlsanity.com/paper/2210.07714/) (2022)
- [Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning](https://mlsanity.com/paper/2110.00394/) (2021)
- [IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation](https://mlsanity.com/paper/2204.08467/) (2022)
- [One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis](https://mlsanity.com/paper/2207.06509/) (2022)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Memory-aware curriculum federated learning for breast cancer classification](https://mlsanity.com/paper/2107.02504/) (2021)
- [Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation](https://mlsanity.com/paper/2104.10195/) (2021)
- [Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease](https://mlsanity.com/paper/2107.03901/) (2021)
- [Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption](https://mlsanity.com/paper/2108.03437/) (2021)
- [Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation](https://mlsanity.com/paper/2203.06338/) (2022)
- [Federated Multi-organ Segmentation with Inconsistent Labels](https://mlsanity.com/paper/2206.07156/) (2022)
- [Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance](https://mlsanity.com/paper/2206.13079/) (2022)
- [The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape](https://mlsanity.com/paper/2405.03636/) (2024)
- [Addressing catastrophic forgetting for medical domain expansion](https://mlsanity.com/paper/2103.13511/) (2021)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification](https://mlsanity.com/paper/2110.08378/) (2021)

## Abstract (arXiv)

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data.