https://github.com/albarqouni/Federated-Learning-In-Healthcare

A list of papers on Federated Deep Learning in Healthcare, in particular, algorithms Deep Learning with Medical Imaging.
https://github.com/albarqouni/Federated-Learning-In-Healthcare

Last synced: 27 days ago
JSON representation

A list of papers on Federated Deep Learning in Healthcare, in particular, algorithms Deep Learning with Medical Imaging.

• Host: GitHub
• URL: https://github.com/albarqouni/Federated-Learning-In-Healthcare
• Owner: albarqouni
• License: mit
• Created: 2020-08-13T17:07:50.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-09-21T12:15:58.000Z (about 4 years ago)
• Last Synced: 2024-11-12T20:16:00.676Z (30 days ago)
• Size: 3.91 KB
• Stars: 31
• Watchers: 3
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-Federated-Learning - 10-

README

        
# Federated-Learning-In-Healthcare

## Background

To the best of my knowledge, this is the first list of federated deep learning papers in healthcare. There are couple of lists for federated learning papers in general, or computer vision, for example [Awesome-Federated-Learning](https://github.com/chaoyanghe/Awesome-Federated-Learning). In this list, I try to classify the papers based on the common challenges in federated deep learning. I believe this list could be a good starting point for FL researchers in Healthcare. 

## Criteria

1. A list of **top federated deep learning papers** published since 2016.

2. Papers are collected from peer-reviewed journals and high reputed conferences. However, it might have recent papers on arXiv. 

3. A meta-data is required along the paper, e.g. topic.

4. Some fundamental papers could be listed here as well. 

*List of Journals / Conferences (J/C):*

- **[Medical Image Analysis (MedIA)](https://www.journals.elsevier.com/medical-image-analysis/)**

- **[IEEE Transaction on Medical Imaging (IEEE-TMI)](https://ieee-tmi.org/)**

- **[IEEE Transaction on Biomedical Engineering (IEEE-TBME)](http://tbme.embs.org/)**

- **[IEEE Journal of Biomedical and Health Informatics (IEEE-JBHI)](http://jbhi.embs.org/)**

- **International Conference on Information Processing in Medical Imaging (IPMI)**

- **International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)**

- **International Conference on Medical Imaging with Deep Learning (MIDL)**

- **IEEE International Symposium on Biomedical Imaging (ISBI)**

## List

| *Topic* | *No* | *Title* | *Conference/Journal* | *Link* |

|--------|------| ------------------------------------------| ----------------------|-------| 

| Intro. to FL | 1 | FedAvg: Communication-Efficient Learning of Deep Networks from Decentralized Data | AISTATS, 2016| [arXiv](https://arxiv.org/abs/1602.05629) |

|  | 2 | The Future of Digital Health with Federated Learning | arXiv, 2020 |   [arXiv](https://arxiv.org/pdf/2003.08119.pdf) |

| Challenges | 3 | Federated Learning: Challenges, Methods, and Future Directions | IEEE Signal Processing Magazine, 2020 |  [arXiv](https://arxiv.org/pdf/1908.07873.pdf) |

|  | 4 | On the Convergence of FedAvg on Non-IID Data | ICLR 2020 | [PDF](https://openreview.net/pdf?id=HJxNAnVtDS) |

| Data Heterogeneity I | 5 | FedMA: Federated Learning with Matched Averaging  | ICLR 2020 | [PDF](https://openreview.net/pdf?id=BkluqlSFDS) |

|  | 6 | Federated Adversarial Domain Adaptation | ICLR 2020 |   [PDF](https://openreview.net/pdf?id=HJezF3VYPB) |

|  | 7 | Federated optimization in heterogeneous networks |  |  [PDF](https://arxiv.org/pdf/1812.06127.pdf) |

|  | 8 | FedAwS: Federated Learning with Only Positive Labels | ICML 2020  |[PDF](https://proceedings.icml.cc/static/paper_files/icml/2020/5034-Paper.pdf)|

|  | 9 | SCAFFOLD: Stochastic Controlled Averaging for Federated Learning | ICML 2020 |  [PDF](https://proceedings.icml.cc/static/paper_files/icml/2020/788-Paper.pdf) |

|  | 10 | Federated Visual Classification with Real-World Data Distribution | CVPR 2020 |   |

| System Heterogeneity | 11 | Federated Multi-Task Learning | NeurIPS 2017 |  [PDF](https://papers.nips.cc/paper/7029-federated-multi-task-learning.pdf) |

|  | 12 | Variational Federated Multi-Task Learning | arXiv 2019 |  [arXiv](https://arxiv.org/pdf/1906.06268.pdf) |

| Privacy-Issues | 13 | Secure, privacy-preserving and federated machine learning in medical imaging | Nature MI |  [HTML](https://www.nature.com/articles/s42256-020-0186-1) |

|  | 14 | Differentially Private Meta-Learning | ICLR 2020 |   [PDF](https://iclr.cc/virtual/poster_rJgqMRVYvr.html) |

| Explainability and Robustness | 15 |  |  |   |

|  | 16 | DBA: Distributed Backdoor Attacks against Federated Learning | ICLR 2020 |  [PDF](http://www.openreview.net/pdf?id=rkgyS0VFvr) |

| Open Problems in FL | -- | Advances and Open Problems in Federated Learning | arXiv |  [PDF](https://arxiv.org/pdf/1912.04977.pdf) |

| Federated Learning with Medical Imaging  | 17 | Privacy-preserving Federated Brain Tumour Segmentation | MICCAIW 2019 |  [HTML](https://link.springer.com/chapter/10.1007/978-3-030-32692-0_16) |

|  | 18 | Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation | MICCAIW 2019 | [HTML](https://link.springer.com/chapter/10.1007/978-3-030-11723-8_9) |

|  | 19 | Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data | ISBI 2019 | [HTML](https://ieeexplore.ieee.org/abstract/document/8759317?casa_token=n3-x19MurqwAAAAA:Eyz2sIgH5MPRzVgtV9ADzDrl_A97A7M6xUYqi3iReri0d-SisH0CYfPYEh8aYjbSwEGHP45n) |

|  | 20 | Inverse Distance Aggregation for Federated Learning with Non-IID Data | MICCAIW 2020 | [PDF](https://arxiv.org/abs/2008.07665) |