# Evaluating Federated Learning approaches for mammography under breast density heterogeneity

arXiv: https://arxiv.org/abs/2605.09137 · HTML: https://mlsanity.com/paper/2605.09137/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 51 references that are in the map.
- First posted on arXiv 9 May 2026.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs](https://mlsanity.com/paper/1802.10026/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [NVIDIA FLARE: Federated Learning from Simulation to Real-World](https://mlsanity.com/paper/2210.13291/) (2022)
- [A Unified Theory of Diversity in Ensemble Learning](https://mlsanity.com/paper/2301.03962/) (2023)
- [Federated Learning for Breast Density Classification: A Real-World Implementation](https://mlsanity.com/paper/2009.01871/) (2020)
- [Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification](https://mlsanity.com/paper/1612.05968/) (2016)
- [Memory-aware curriculum federated learning for breast cancer classification](https://mlsanity.com/paper/2107.02504/) (2021)
- [Memory-Efficient Implementation of DenseNets](https://mlsanity.com/paper/1707.06990/) (2017)
- [Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network](https://mlsanity.com/paper/2110.01606/) (2021)
- [Deformable Gabor Feature Networks for Biomedical Image Classification](https://mlsanity.com/paper/2012.04109/) (2020)
- [Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Net](https://mlsanity.com/paper/1904.12319/) (2019)
- [MammoFL: Mammographic Breast Density Estimation using Federated Learning](https://mlsanity.com/paper/2206.05575/) (2022)

## Abstract (arXiv)

Breast density is a key factor that influences mammography interpretation and is a major source of heterogeneity in multicenter datasets. Such heterogeneity poses challenges for collaborative machine learning across institutions, particularly in Federated Learning. This study aims to evaluate the impact of breast density-induced heterogeneity on FL for mammography image classification and to assess the robustness of common FL algorithms in realistic clinical settings. We conducted experiments under two scenarios: (1) a strongly heterogeneous setting where each participating site contributed exclusively low- or high-density cases, based on the BI-RADS density score, and (2) a population-based setting simulating breast density distributions in White and Asian populations. For the strongly heterogeneous setting, we evaluated two configurations: one with 2 clients, where the cases were grouped as BI-RADS A-B and C-D, and one with 4 clients, where each site contained cases of a single BI-RADS density. We compared three FL methods (FedAvg, FedProx, SCAFFOLD) against centralized training, local-only training, and naive aggregation approaches, including ensembling and weight averaging. Across both scenarios, FL achieved performance comparable to centralized training, while local models and naive aggregation approaches underperformed in the presence of strong heterogeneity. Notably, FedAvg achieved accuracy on par with or exceeding centralized training, demonstrating resilience to breast density-induced data imbalance without requiring specialized heterogeneity mitigation algorithms. These findings show that FL can address breast density-related heterogeneity, supporting its feasibility for real-world mammography workflows. The demonstrated robustness of FedAvg underscores the potential for broad clinical deployment of FL, enabling collaborative model development while maintaining data privacy.