# Secure Federated Learning Approaches to Diagnosing COVID-19

arXiv: https://arxiv.org/abs/2401.12438 · HTML: https://mlsanity.com/paper/2401.12438/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 39 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [COVID-19 Image Data Collection: Prospective Predictions Are the Future](https://mlsanity.com/paper/2006.11988/) (2020)
- [A Deeper Look at Dataset Bias](https://mlsanity.com/paper/1505.01257/) (2015)
- [Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning](https://mlsanity.com/paper/2004.09363/) (2020)
- [CrypTFlow: Secure TensorFlow Inference](https://mlsanity.com/paper/1909.07814/) (2019)
- [Differential Privacy-enabled Federated Learning for Sensitive Health Data](https://mlsanity.com/paper/1910.02578/) (2019)
- [Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling](https://mlsanity.com/paper/2005.14480/) (2020)
- [COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images](https://mlsanity.com/paper/2006.01409/) (2020)
- [Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays](https://mlsanity.com/paper/2004.08379/) (2020)

## Built on by (most cited first, 1 of 1)

- [Privacy-Preserving Chest X-ray Report Generation via Multimodal Federated Learning with ViT and GPT-2](https://mlsanity.com/paper/2505.21715/) (2025)

## Abstract (arXiv)

The recent pandemic has underscored the importance of accurately diagnosing COVID-19 in hospital settings. A major challenge in this regard is differentiating COVID-19 from other respiratory illnesses based on chest X-rays, compounded by the restrictions of HIPAA compliance which limit the comparison of patient X-rays. This paper introduces a HIPAA-compliant model to aid in the diagnosis of COVID-19, utilizing federated learning. Federated learning is a distributed machine learning approach that allows for algorithm training across multiple decentralized devices using local data samples, without the need for data sharing. Our model advances previous efforts in chest X-ray diagnostic models. We examined leading models from established competitions in this domain and developed our own models tailored to be effective with specific hospital data. Considering the model's operation in a federated learning context, we explored the potential impact of biased data updates on the model's performance. To enhance hospital understanding of the model's decision-making process and to verify that the model is not focusing on irrelevant features, we employed a visualization technique that highlights key features in chest X-rays indicative of a positive COVID-19 diagnosis.