# Experiments of Federated Learning for COVID-19 Chest X-ray Images

arXiv: https://arxiv.org/abs/2007.05592 · HTML: https://mlsanity.com/paper/2007.05592/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 30 of 31)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Federated Learning for Internet of Things: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2111.07494/) (2021)
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- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2203.09702/) (2022)
- [Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease](https://mlsanity.com/paper/2107.03901/) (2021)
- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [Federated Learning for Industrial Internet of Things in Future Industries](https://mlsanity.com/paper/2105.14659/) (2021)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
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- [Advancements in Federated Learning: Models, Methods, and Privacy](https://mlsanity.com/paper/2302.11466/) (2023)
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- [A Federated Learning Approach to Privacy Preserving Offensive Language Identification](https://mlsanity.com/paper/2404.11470/) (2024)
- [A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study](https://mlsanity.com/paper/2203.12803/) (2022)
- [Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading](https://mlsanity.com/paper/2302.06089/) (2023)
- [On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments](https://mlsanity.com/paper/2409.03897/) (2024)
- [Blockchain Meets LLMs: A Living Survey on Bidirectional Integration](https://mlsanity.com/paper/2411.16809/) (2024)
- [Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring](https://mlsanity.com/paper/2105.15094/) (2021)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [DPCOVID: Privacy-Preserving Federated Covid-19 Detection](https://mlsanity.com/paper/2110.13760/) (2021)
- [Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model](https://mlsanity.com/paper/2204.10505/) (2022)
- [Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks](https://mlsanity.com/paper/2310.08681/) (2023)
- [A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications](https://mlsanity.com/paper/2409.15802/) (2024)
- [In-depth Analysis of Privacy Threats in Federated Learning for Medical Data](https://mlsanity.com/paper/2409.18907/) (2024)
- [A Novel Optimized Asynchronous Federated Learning Framework](https://mlsanity.com/paper/2111.09487/) (2021)
- [Privacy-Preserving Online Content Moderation: A Federated Learning Use Case](https://mlsanity.com/paper/2209.11843/) (2022)
- [Bayesian Networks and Machine Learning for COVID-19 Severity Explanation and Demographic Symptom Classification](https://mlsanity.com/paper/2406.10807/) (2024)
- [MV-Adapter: Enhancing Underwater Instance Segmentation via Adaptive Channel Attention](https://mlsanity.com/paper/2411.00472/) (2024)
- [EPIC: Enhancing Privacy through Iterative Collaboration](https://mlsanity.com/paper/2411.05167/) (2024)

## Abstract (arXiv)

AI plays an important role in COVID-19 identification. Computer vision and deep learning techniques can assist in determining COVID-19 infection with Chest X-ray Images. However, for the protection and respect of the privacy of patients, the hospital's specific medical-related data did not allow leakage and sharing without permission. Collecting such training data was a major challenge. To a certain extent, this has caused a lack of sufficient data samples when performing deep learning approaches to detect COVID-19. Federated Learning is an available way to address this issue. It can effectively address the issue of data silos and get a shared model without obtaining local data. In the work, we propose the use of federated learning for COVID-19 data training and deploy experiments to verify the effectiveness. And we also compare performances of four popular models (MobileNet, ResNet18, MoblieNet, and COVID-Net) with the federated learning framework and without the framework. This work aims to inspire more researches on federated learning about COVID-19.