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https://github.com/monk1337/Aweome-Heathcare-Federated-Learning

A curated list of Federated Learning papers/articles and recent advancements.
https://github.com/monk1337/Aweome-Heathcare-Federated-Learning
List: Aweome-Heathcare-Federated-Learning
artifical-intelligense awesome-list awesome-machine-learning blockchain-medical-applications data-privacy deep-learning deep-learning-healthcare distributed-machine-learning federated-learning healthcare-data healthcare-datasets healthcare-federated-learning healthcare-imaging life-science-data machine-learning machine-learning-healthcare machine-learning-privacy medical-data-pipeline non-iid vertical-federated-learning
Last synced: 3 months ago
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A curated list of Federated Learning papers/articles and recent advancements.
Host: GitHub
URL: https://github.com/monk1337/Aweome-Heathcare-Federated-Learning
Owner: monk1337
Created: 2020-12-16T15:10:52.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2023-02-19T15:58:04.000Z (over 1 year ago)
Last Synced: 2024-05-22T23:05:40.338Z (5 months ago)
Topics: artifical-intelligense, awesome-list, awesome-machine-learning, blockchain-medical-applications, data-privacy, deep-learning, deep-learning-healthcare, distributed-machine-learning, federated-learning, healthcare-data, healthcare-datasets, healthcare-federated-learning, healthcare-imaging, life-science-data, machine-learning, machine-learning-healthcare, machine-learning-privacy, medical-data-pipeline, non-iid, vertical-federated-learning
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Forks: 14
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

ultimate-awesome - Aweome-Heathcare-Federated-Learning - A curated list of Federated Learning papers/articles and recent advancements. (Other Lists / PowerShell Lists)
README

        # Awesome Healthcare Federated Learning

Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. This repo contains a curated list of Federated Learning papers/resources and recent advancements in Healthcare.

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

  

# Contribute

We welcome contributions to this list! In fact, that's the main reason why I created it - to encourage contributions and encourage people to subscribe to changes in order to stay informed about new and exciting developments in the world of Heathcare Federated Learning.

Need help in

- Classify the paper into appropriate categories such as [ Survey, Experiment, New Algorithm etc]

- Sort the paper based on publication year

- Add new papers to update the list

Thank you for your interest in contributing to this project!

##### Table of Contents

1. [Papers](#FL-papers)  

2. [Code](#Code)

3. [datasets](#Datasets)

4. [Tutorials](#Tutorials)

5. [Researchers](#Researchers)

## Code

- **Tensorflow Federated** 

  - [[Code]](https://github.com/tensorflow/federated)

  

- **An Industrial Grade Federated Learning Framework**

  - [[Code]](https://github.com/FederatedAI/FATE)

  

- **Flower - A Friendly Federated Learning Framework**

  - [[Code]](https://github.com/adap/flower)

  

- **Data science on data without acquiring a copy**

  - [[Code]](https://github.com/OpenMined/PySyft)

 

  

## Tutorials

- **Federated Learning on Mobile**

  - [[Course]](https://courses.openmined.org/courses/federated-learning-on-mobile)

  

- **Federated Learning with Google**

  - [[Course]](https://federated.withgoogle.com/)

## Papers

### Survey

- **Federated learning for healthcare informatics**

  - Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter Walker, Jiang Bian, Fei Wang

  - [[Paper]](https://arxiv.org/abs/1911.06270)

  

- **The future of digital health with federated learning**

  - Nicola Rieke

  - [[Paper]](https://www.nature.com/articles/s41746-020-00323-1)

- **Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges**

  - Madhura Joshi , Ankit Pal , Malaikannan Sankarasubbu

  - [[Paper]](https://dl.acm.org/doi/10.1145/3533708)

  

  

 - **Federated Learning for Healthcare Informatics1** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659898/) 

- **AI in Health: State of the Art, Challenges, and Future Directions** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/31419814/) 

- **Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697547/) 

- **Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113079/) 

  

- **Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794892/) 

  

 

 ### Covid-19

 

 - **Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19** 

  - [[Paper]](https://www.medrxiv.org/content/10.1101/2020.08.11.20172809v1.full-text) 

  

 - **Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7864789/)

  

  

    

- **Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge** 

  - [[Paper]](https://arxiv.org/pdf/2101.07511.pdf)

  

  

- **The value of federated learning during and post-COVID-19** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7928850/)

  

- **SCOR: A secure international informatics infrastructure to investigate COVID-19** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454652/) 

  

- **Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study** 

  - [[Paper]](https://www.medrxiv.org/content/10.1101/2021.01.14.21249793v1.full-text)

- **COVID-19 IMAGING DATA PRIVACY BY FEDERATED LEARNING DESIGN: A THEORETICAL FRAMEWORK** 

  - [[Paper]](https://arxiv.org/pdf/2010.06177.pdf)

- **Artificial intelligence in COVID-19 drug repurposing** 

  - [[Paper]](https://www.thelancet.com/pdfs/journals/landig/PIIS2589-7500(20)30192-8.pdf) 

- **Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging** 

  - [[Paper]](https://arxiv.org/pdf/2007.06537.pdf) 

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

  - [[Paper]](https://arxiv.org/pdf/2007.05592.pdf) 

  

 

 

### Experiments

  

- **Federated Learning on Clinical Benchmark Data: Performance Assessment**

  - Geun Hyeong Lee and Soo-Yong Shin

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7652692/)

  

- **Secure and Robust Machine Learning for Health Care**

  - Adnan Qayyum, Junaid Qadir, Muhammad Bilal, Ala Al-Fuqaha

  - [[Paper]](https://arxiv.org/abs/2001.08103)

  

- **Privacy-first health research with federated learning** 

  - [[Paper]](https://www.medrxiv.org/content/10.1101/2020.12.22.20245407v1.full-text) 

- **Patch-Based Surface Morphometry Feature Selection with Federated Group Lasso Regression** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694696/) 

- **Predicting Adverse Drug Reactions on Distributed Health Data using Federated Learning** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153050/) 

- **Federated electronic health records research technology to support clinical trial protocol optimization: Evidence from EHR4CR and the InSite platform** 

  - [[Paper]](https://www.sciencedirect.com/science/article/pii/S1532046418302314?via%3Dihub) 

- **Probabilistic Predictions with Federated Learning** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7823259/) 

- **Using federated data sources and Varian Learning Portal framework to train a neural network model for automatic organ segmentation** 

  - [[Paper]](https://www.physicamedica.com/article/S1120-1797(20)30065-X/fulltext) 

- **Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085801/) 

- **Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning** 

  - [[Paper]](https://papers.nips.cc/paper/2018/file/feecee9f1643651799ede2740927317a-Paper.pdf) 

- **Privacy-preserving model learning on a blockchain network-of-networks** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7025358/) 

- **Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712958/) 

- **Healthchain: A novel framework on privacy preservation of electronic health records using blockchain technology** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725426/) 

- **Privacy-Preserving in Healthcare Blockchain Systems Based on Lightweight Message Sharing** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180853/) 

- **MedBlock: Efficient and Secure Medical Data Sharing Via Blockchain** 

  - [[Paper]](https://link.springer.com/article/10.1007%2Fs10916-018-0993-7) 

- **Blockchain distributed ledger technologies for biomedical and health care applications** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6080687/) 

- **A Decentralized Privacy-Preserving Healthcare Blockchain for IoT** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359727/) 

- **Joint Imaging Platform for Federated Clinical Data Analytics** 

  - [[Paper]](https://ascopubs.org/doi/10.1200/CCI.20.00045?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed&) 

- **Federated Transfer Learning for EEG Signal Classification** 

  - [[Paper]](https://arxiv.org/pdf/2004.12321.pdf) 

- **Federated Learning used for predicting outcomes in SARS-COV-2 patients** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805458/) 

- **Large-Scale Water Quality Prediction Using Federated Sensing and Learning: A Case Study with Real-World Sensing Big-Data** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7923429/) 

- **Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results** 

  - [[Paper]](https://reader.elsevier.com/reader/sd/pii/S1361841520301298?token=49EC74DDB6FB2CAE2701353194B68C2D3ED8687C6D0C7674CCAFE61148A2E304204AF7863116946239671695D825D2C4) 

- **Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7387485/) 

- **Security and privacy requirements for a multi-institutional cancer research data grid: an interview-based study** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2709611/) 

- **Federated learning of predictive models from federated Electronic Health Records** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5836813/) 

- **Creating a data exchange strategy for radiotherapy research: towards federated databases and anonymised public datasets** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4648243/) 

- **Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7909896/) 

- **Balancing Accuracy and Privacy in Federated Queries of Clinical Data Repositories: Algorithm Development and Validation** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671849/) 

- **A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540462/) 

- **Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7238077/) 

- **A review on the state-of-the-art privacy-preserving approaches in the e-health clouds** 

  - [[Paper]](https://silo.tips/download/a-review-on-the-state-of-the-art-privacy-preserving-approaches-in-the-e-health-c) 

- **eHealth Cloud Security Challenges: A Survey** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6745146/) 

- **Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5833935/) 

- **Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5575360/) 

- **Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept** 

  - [[Paper]](https://www.thegreenjournal.com/article/S0167-8140(16)34336-5/fulltext) 

- **How Should Health Data Be Used?** 

  - [[Paper]](https://bioethics.yale.edu/sites/default/files/files/ISPS14-025.pdf) 

- **Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes** 

  - [[Paper]](https://www.sciencedirect.com/science/article/pii/S0933365717306218?via%3Dihub) 

- **Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling** 

  - [[Paper]](https://sci-hub.mksa.top/10.1016/j.jbi.2020.103424) 

- **A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6856029/) 

- **Multi-center machine learning in imaging psychiatry: A meta-model approach** 

  - [[Paper]](https://sci-hub.mksa.top/10.1016/j.neuroimage.2017.03.027) 

- **A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5081132/) 

- **Distributed deep learning networks among institutions for medical imaging** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6077811/) 

- **The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382618/) 

- **WebDISCO: a web service for distributed cox model learning without patient-level data sharing** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009917/) 

- **Differentially Private Distributed Online Learning** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6764830/) 

- **An Uplink Communication-Efficient Approach to Featurewise Distributed Sparse Optimization With Differential Privacy** 

  - [[Paper]](https://sci-hub.mksa.top/10.1109/tnnls.2020.3020955) 

- **A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5598559/) 

- **Secure Multiparty Quantum Computation for Summation and Multiplication** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726197/) 

- **Hybrid Quantum Protocols for Secure Multiparty Summation and Multiplication** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7272455/) 

- **A blockchain-based scheme for privacy-preserving and secure sharing of medical data** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462555/) 

- **Cost-Efficient and Multi-Functional Secure Aggregation in Large Scale Distributed Application** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4994945/) 

- **Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5038741/) 

- **Security issues in healthcare applications using wireless medical sensor networks: a survey** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3279202/) 

- **A secure distributed logistic regression protocol for the detection of rare adverse drug events** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3628043/) 

- **High performance logistic regression for privacy-preserving genome analysis** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7818577/) 

- **Efficient Privacy-Preserving Access Control Scheme in Electronic Health Records System** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210245/) 

- **Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6658266/) 

- **Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7025371/) 

- **DPSynthesizer: Differentially Private Data Synthesizer for Privacy Preserving Data Sharing** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/26167358/) 

- **A flexible approach to distributed data anonymization** 

  - [[Paper]](https://www.sciencedirect.com/science/article/pii/S1532046413001937?via%3Dihub) 

- **Privacy-preserving data cube for electronic medical records: An experimental evaluation** 

  - [[Paper]](https://sci-hub.mksa.top/10.1016/j.ijmedinf.2016.09.008) 

- **A framework to preserve the privacy of electronic health data streams** 

  - [[Paper]](https://www.sciencedirect.com/science/article/pii/S1532046414000823?via%3Dihub) 

- **Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5209873/) 

- **Design and implementation of a privacy preserving electronic health record linkage tool in Chicago** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009931/) 

- **Privacy preserving interactive record linkage (PPIRL)** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932473/) 

- **Privacy-preserving record linkage in large databases using secure multiparty computation** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6180364/) 

- **Sample Complexity Bounds for Differentially Private Learning** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4183222/) 

- **Convergence Rates for Differentially Private Statistical Estimation** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4188376/) 

- **Efficient differentially private learning improves drug sensitivity prediction** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5801888/) 

- **A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7763193/) 

- **Privacy-preserving aggregation of personal health data streams** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264901/) 

- **Efficient and Privacy-Preserving Online Medical Prediagnosis Framework Using Nonlinear SVM** 

  - [[Paper]](https://sci-hub.mksa.top/10.1109/jbhi.2016.2548248) 

- **Privacy-preserving biomedical data dissemination via a hybrid approach** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371369/) 

- **A community effort to protect genomic data sharing, collaboration and outsourcing** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677972/) 

- **Privacy challenges and research opportunities for genomic data sharing** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7761157/) 

- **Privacy-Preserving Integration of Medical Data : A Practical Multiparty Private Set Intersection** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5239815/) 

- **Secure multiparty computation for privacy-preserving drug discovery** 

  - [[Paper]](https://sci-hub.mksa.top/10.1093/bioinformatics/btaa038) 

- **Privacy-Preserving Cost-Sensitive Learning** 

  - [[Paper]](https://sci-hub.mksa.top/10.1109/TNNLS.2020.2996972) 

- **Differentially Private Empirical Risk Minimization** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3164588/) 

- **Privacy-preserving heterogeneous health data sharing** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3628047/) 

- **A comprehensive tool for creating and evaluating privacy-preserving biomedical prediction models** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7014648/) 

- **Privacy-enhancing ETL-processes for biomedical data** 

  - [[Paper]](https://www.sciencedirect.com/science/article/pii/S1386505618307007?via%3Dihub) 

- **Privacy-preserving restricted boltzmann machine** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4094866/) 

- **Privacy preserving processing of genomic data: A survey** 

  - [[Paper]](https://www.sciencedirect.com/science/article/pii/S1532046415001100?via%3Dihub) 

- **How (not) to protect genomic data privacy in a distributed network: using trail re-identification to evaluate and design anonymity protection systems** 

  - [[Paper]](https://www.sciencedirect.com/science/article/pii/S153204640400053X?via%3Dihub) 

- **Are privacy-enhancing technologies for genomic data ready for the clinic? A survey of medical experts of the Swiss HIV Cohort Study** 

  - [[Paper]](https://www.sciencedirect.com/science/article/pii/S1532046417302836?via%3Dihub) 

- **Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5266631/) 

- **The tension between data sharing and the protection of privacy in genomics research** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4337968/) 

- **ConTPL: Controlling Temporal Privacy Leakage in Differentially Private Continuous Data Release** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697134/) 

- **New threats to health data privacy** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3247088/) 

- **Securing electronic health records without impeding the flow of information** 

  - [[Paper]](https://sci-hub.mksa.top/10.1016/j.ijmedinf.2006.09.015) 

- **How to Accurately and Privately Identify Anomalies** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927253/) 

- **A Guide for Private Outlier Analysis** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423021/) 

- **Privacy-Aware Distributed Hypothesis Testing** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517198/) 

- **Distributed Hypothesis Testing with Privacy Constraints** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514967/) 

- **Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400044/) 

- **Web-Based Privacy-Preserving Multicenter Medical Data Analysis Tools Via Threshold Homomorphic Encryption: Design and Development Study** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7755539/) 

- **A Privacy-Preserving Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6971161/) 

- **Privacy-enhanced multi-party deep learning** 

  - [[Paper]](https://sci-hub.mksa.top/10.1016/j.neunet.2019.10.001) 

- **Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523633/) 

- **Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5924379/) 

- **A Critical Evaluation of Privacy and Security Threats in Federated Learning** 

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7765278/) 

- **Properties of a federated epidemiology query system** 

  - [[Paper]](https://sci-hub.mksa.top/10.1016/j.ijmedinf.2006.05.040) 

- **Advanced and secure architectural EHR approaches** 

  - [[Paper]](https://sci-hub.mksa.top/10.1016/j.ijmedinf.2005.07.017) 

- **Implementing security in a distributed web-based EHCR** 

  - [[Paper]](https://sci-hub.mksa.top/10.1016/j.ijmedinf.2006.09.017) 

- **Health information systems - past, present, future** 

  - [[Paper]](https://sci-hub.mksa.top/10.1016/j.ijmedinf.2005.08.002) 

- **Federated healthcare record server--the Synapses paradigm** 

  - [[Paper]](https://sci-hub.mksa.top/10.1016/s1386-5056(98)00121-x) 

- **The basic principles of the synapses federated healthcare record server** 

  - [[Paper]](https://sci-hub.mksa.top/10.1016/s1386-5056(98)00131-2) 

- **Ternary Compression for Communication-Efficient Federated Learning** 

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- **A NOVEL APPROACH TO MACHINE LEARNING APPLICATION TO PROTECTION PRIVACY DATA IN HEALTHCARE: FEDERATED LEARNING** 

  - [[Paper]](http://cms.galenos.com.tr/Uploads/Article_40337/nkmj-8-22-En.pdf) 

- **FEEL: A Federated Edge Learning System for Efficient and Privacy-Preserving Mobile Healthcare** 

  - [[Paper]](https://dl.acm.org/doi/10.1145/3404397.3404410) 

- **VAFL: a Method of Vertical Asynchronous Federated Learning** 

  - [[Paper]](https://arxiv.org/pdf/2007.06081.pdf) 

- **Anonymizing Data for Privacy-Preserving Federated Learning** 

  - [[Paper]](https://arxiv.org/pdf/2002.09096.pdf) 

- **FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning** 

  - [[Paper]](https://arxiv.org/pdf/2003.09288.pdf)

- **Modelling Audiological Preferences using Federated Learning** 

  - [[Paper]](https://dl.acm.org/doi/10.1145/3386392.3399560) 

- **Privacy-first health research with federated learning** 

  - [[Paper]](https://www.medrxiv.org/content/medrxiv/early/2020/12/24/2020.12.22.20245407.full.pdf) 

- **A Syntactic Approach for Privacy-Preserving Federated Learning** 

  - [[Paper]](http://ecai2020.eu/papers/1591_paper.pdf) 

- **Achieving Privacy-preserving Federated Learning with Irrelevant Updates over E-Health Applications** 

  - [[Paper]](https://ieeexplore.ieee.org/document/9149385) 

- **FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring** 

  - [[Paper]](https://arxiv.org/pdf/2012.07450.pdf) 

- **A Federated Learning Framework for Privacy-preserving and Parallel Training** 

  - [[Paper]](https://arxiv.org/pdf/2001.09782.pdf)

  

- **Attack Detection Using Federated Learning in Medical Cyber-Physical Systems** 

  - [[Paper]](http://faculty.washington.edu/geetha/Papers/fedlearningIDS.pdf) 

- **Dealing with Open Issues and Unmet Needs in Healthcare Through Ontology Matching and Federated Learning** 

  - [[Paper]](https://link.springer.com/chapter/10.1007%2F978-3-030-64610-3_36) 

- **Federated Learning used for predicting outcomes in SARS-COV-2 patients** 

  - [[Paper]](https://www.researchsquare.com/article/rs-126892/v1) 

- **FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record** 

  - [[Paper]](https://arxiv.org/pdf/1811.11400.pdf) 

- **Personalized Federated Deep Learning for Pain Estimation From Face Images** 

  - [[Paper]](https://arxiv.org/pdf/2101.04800.pdf) 

- **Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare** 

  - [[Paper]](https://arxiv.org/pdf/2012.12591.pdf) 

- **Reproduce Distributed Learning Networks for Medical Imaging and Investigate the Performance in Edge Scenarios (Healthcare)** 

  - [[Paper]](http://cs230.stanford.edu/projects_winter_2020/reports/32460513.pdf) 

- **DNet: An Efficient Privacy-Preserving Distributed Learning Framework for Healthcare Systems** 

  - [[Paper]](https://link.springer.com/chapter/10.1007%2F978-3-030-65621-8_9) 

- **A pseudonymisation protocol with implicit and explicit consent routes for health records in federated ledgers** 

  - [[Paper]](https://ieeexplore.ieee.org/document/9211712) 

- **Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics** 

  - [[Paper]](https://pdfs.semanticscholar.org/cd55/5d2586e893f6e90d02ba38e6f4b88611d060.pdf?_ga=2.132743822.128127665.1616063254-2119887776.1616063254) 

- **A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services** 

  - [[Paper]](https://www.mdpi.com/1424-8220/21/2/552) 

- **The Evolution of a Healthcare Software Framework: Reuse, Evaluation and Lessons Learned** 

  - [[Paper]](https://annals-csis.org/proceedings/2018/drp/pdf/173.pdf) 

- **Confederated learning in healthcare: training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale Health System Intelligence** 

  - [[Paper]](https://arxiv.org/pdf/1910.02109.pdf) 

- **Towards a Keyword Extraction in Medical and Healthcare Education** 

  - [[Paper]](https://annals-csis.org/proceedings/2017/drp/pdf/351.pdf)

 

- **From the Data on Many, Precision Medicine for “One”: The Case for Widespread Genomic Data Sharing** 

  - [[Paper]](https://karger.com/Article/FullText/481682) 

- **Federated Learning in Mobile Edge Networks: A Comprehensive Survey** 

  - [[Paper]](https://arxiv.org/pdf/1909.11875.pdf) 

- **DBA: Distributed Backdoor Attacks against Federated Learning** 

  - [[Paper]](https://openreview.net/forum?id=rkgyS0VFvr) 

- **Three Approaches for Personalization with Applications to Federated Learning** 

  - [[Paper]](https://arxiv.org/pdf/2002.10619.pdf) 

- **Federated Learning of a Mixture of Global and Local Models** 

  - [[Paper]](https://arxiv.org/pdf/2002.05516.pdf) 

- **Think Locally, Act Globally: Federated Learning with Local and Global Representations** 

  - [[Paper]](https://arxiv.org/pdf/2001.01523.pdf) 

- **Inverting Gradients - How easy is it to break privacy in federated learning?** 

  - [[Paper]](https://arxiv.org/pdf/2003.14053.pdf) 

- **A Framework for Evaluating Gradient Leakage Attacks in Federated Learning** 

  - [[Paper]](https://arxiv.org/pdf/2004.10397.pdf) 

- **Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results** 

  - [[Paper]](https://www.sciencedirect.com/science/article/pii/S1361841520301298?via%3Dihub) 

- **Federated learning in medicine: facilitating multi‑institutional collaborations without sharing patient data** 

  - [[Paper]](https://www.nature.com/articles/s41598-020-69250-1.pdf)

  

- **Multi-Center Federated Learning** 

  - [[Paper]](https://arxiv.org/pdf/2005.01026.pdf) 

- **Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges** 

  - [[Paper]](https://arxiv.org/pdf/2009.13012.pdf) 

  

- **VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange**

  - [[Paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075508/)

- **One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis**

  - [[Paper]](https://arxiv.org/abs/2207.06509) - [[Code]](https://github.com/icon-lab/pFLSynth)

- **Federated Learning of Generative Image Priors for MRI Reconstruction**

  - [[Paper]](https://arxiv.org/abs/2202.04175) - [[Code]](https://github.com/icon-lab/FedGIMP)

  

## Datasets

- **Federated Learning framework to preserve privacy**

  - [[Code]](https://github.com/ivishalanand/Federated-Learning-on-Hospital-Data)

[[Image source]](https://blog.ml.cmu.edu/2019/11/12/federated-learning-challenges-methods-and-future-directions/)