# FedDropoutAvg: Generalizable federated learning for histopathology image classification

arXiv: https://arxiv.org/abs/2111.13230 · HTML: https://mlsanity.com/paper/2111.13230/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 25 Nov 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [The Non-IID Data Quagmire of Decentralized Machine Learning](https://mlsanity.com/paper/1910.00189/) (2019)
- [Privacy-preserving Federated Brain Tumour Segmentation](https://mlsanity.com/paper/1910.00962/) (2019)
- [Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://mlsanity.com/paper/1812.07210/) (2018)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results](https://mlsanity.com/paper/2001.05647/) (2020)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [Federated Learning for Breast Density Classification: A Real-World Implementation](https://mlsanity.com/paper/2009.01871/) (2020)
- [Siloed Federated Learning for Multi-Centric Histopathology Datasets](https://mlsanity.com/paper/2008.07424/) (2020)
- [Federated Learning for Computational Pathology on Gigapixel Whole Slide Images](https://mlsanity.com/paper/2009.10190/) (2020)
- [Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images](https://mlsanity.com/paper/1907.09478/) (2019)
- [Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning](https://mlsanity.com/paper/2011.04050/) (2020)
- [Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning](https://mlsanity.com/paper/2009.13148/) (2020)

## Built on by (most cited first, 7 of 7)

- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [CrowdGuard: Federated Backdoor Detection in Federated Learning](https://mlsanity.com/paper/2210.07714/) (2022)
- [FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning](https://mlsanity.com/paper/2208.05174/) (2022)
- [Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout](https://mlsanity.com/paper/2302.11485/) (2023)
- [Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations](https://mlsanity.com/paper/2306.03600/) (2023)
- [A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets](https://mlsanity.com/paper/2309.16825/) (2023)
- [Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2503.11146/) (2025)

## Abstract (arXiv)

Federated learning (FL) enables collaborative learning of a deep learning model without sharing the data of participating sites. FL in medical image analysis tasks is relatively new and open for enhancements. In this study, we propose FedDropoutAvg, a new federated learning approach for training a generalizable model. The proposed method takes advantage of randomness, both in client selection and also in federated averaging process. We compare FedDropoutAvg to several algorithms in an FL scenario for real-world multi-site histopathology image classification task. We show that with FedDropoutAvg, the final model can achieve performance better than other FL approaches and closer to a classical deep learning model that requires all data to be shared for centralized training. We test the trained models on a large dataset consisting of 1.2 million image tiles from 21 different centers. To evaluate the generalization ability of the proposed approach, we use held-out test sets from centers whose data was used in the FL and for unseen data from other independent centers whose data was not used in the federated training. We show that the proposed approach is more generalizable than other state-of-the-art federated training approaches. To the best of our knowledge, ours is the first study to use a randomized client and local model parameter selection procedure in a federated setting for a medical image analysis task.