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A curated list of Federated Learning papers/articles and recent advancements.

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 PRs Welcome License: MIT

Table of Contents
  1. Papers
  2. Code
  3. datasets
  4. Tutorials
  5. Researchers

Papers

  • Federated learning for healthcare informatics

    • Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter Walker, Jiang Bian, Fei Wang
    • [Paper]

  • The future of digital health with federated learning

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

    • Madhura Joshi , Ankit Pal , Malaikannan Sankarasubbu
    • [Paper]

  • Federated Learning on Clinical Benchmark Data: Performance Assessment

    • Geun Hyeong Lee and Soo-Yong Shin
    • [Paper]

  • Secure and Robust Machine Learning for Health Care

    • Adnan Qayyum, Junaid Qadir, Muhammad Bilal, Ala Al-Fuqaha
    • [Paper]

  • Federated Learning for Healthcare Informatics1

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

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

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

  • Privacy-first health research with federated learning

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

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

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

  • Predicting Adverse Drug Reactions on Distributed Health Data using Federated Learning

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

  • Probabilistic Predictions with Federated Learning

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

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

  • Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices

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

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

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

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

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

  • MedBlock: Efficient and Secure Medical Data Sharing Via Blockchain

  • Blockchain distributed ledger technologies for biomedical and health care applications

  • A Decentralized Privacy-Preserving Healthcare Blockchain for IoT

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

  • Joint Imaging Platform for Federated Clinical Data Analytics

  • Federated Transfer Learning for EEG Signal Classification

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

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

  • Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

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

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

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

  • Federated learning of predictive models from federated Electronic Health Records

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

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

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

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

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

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

  • eHealth Cloud Security Challenges: A Survey

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

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

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

  • How Should Health Data Be Used?

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

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

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

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

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

  • Distributed deep learning networks among institutions for medical imaging

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

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

  • Differentially Private Distributed Online Learning

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

  • A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy

  • Secure Multiparty Quantum Computation for Summation and Multiplication

  • Hybrid Quantum Protocols for Secure Multiparty Summation and Multiplication

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

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

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

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

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

  • High performance logistic regression for privacy-preserving genome analysis

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

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

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

  • DPSynthesizer: Differentially Private Data Synthesizer for Privacy Preserving Data Sharing

  • A flexible approach to distributed data anonymization

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

  • A framework to preserve the privacy of electronic health data streams

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

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

  • Privacy preserving interactive record linkage (PPIRL)

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

  • Sample Complexity Bounds for Differentially Private Learning

  • Convergence Rates for Differentially Private Statistical Estimation

  • Efficient differentially private learning improves drug sensitivity prediction

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

  • Privacy-preserving aggregation of personal health data streams

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

  • Privacy-preserving biomedical data dissemination via a hybrid approach

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

  • Privacy challenges and research opportunities for genomic data sharing

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

  • Secure multiparty computation for privacy-preserving drug discovery

  • Privacy-Preserving Cost-Sensitive Learning

  • Differentially Private Empirical Risk Minimization

  • Privacy-preserving heterogeneous health data sharing

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

  • Privacy-enhancing ETL-processes for biomedical data

  • Privacy-preserving restricted boltzmann machine

  • Privacy preserving processing of genomic data: A survey

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

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

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

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

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

  • New threats to health data privacy

  • Securing electronic health records without impeding the flow of information

  • How to Accurately and Privately Identify Anomalies

  • A Guide for Private Outlier Analysis

  • Privacy-Aware Distributed Hypothesis Testing

  • Distributed Hypothesis Testing with Privacy Constraints

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

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

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

  • Privacy-enhanced multi-party deep learning

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

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

  • A Critical Evaluation of Privacy and Security Threats in Federated Learning

  • Properties of a federated epidemiology query system

  • Advanced and secure architectural EHR approaches

  • Implementing security in a distributed web-based EHCR

  • Health information systems - past, present, future

  • Federated healthcare record server--the Synapses paradigm

  • The basic principles of the synapses federated healthcare record server

  • Ternary Compression for Communication-Efficient Federated Learning

  • Distributed learning on 20 000+ lung cancer patients - The Personal Health Train

  • A Distributed Ensemble Approach for Mining Healthcare Data under Privacy Constraints

  • FeARH: Federated machine learning with Anonymous Random Hybridization on electronic medical records

  • Smart Medical Information Technology for Healthcare (SMITH)

  • Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records

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

  • Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data

  • Federated Tensor Factorization for Computational Phenotyping

  • Cloud-Based Federated Learning Implementation Across Medical Centers

  • ACTION-EHR: Patient-Centric Blockchain-Based Electronic Health Record Data Management for Cancer Care

  • Federated learning improves site performance in multicenter deep learning without data sharing

  • Healthcare information exchange system based on a hybrid central/federated model

  • Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies

  • Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation

  • The future of digital health with federated learning

  • A novel privacy-preserving federated genome-wide association study framework and its application in identifying potential risk variants in ankylosing spondylitis

  • Privacy-preserving GWAS analysis on federated genomic datasets

  • SAFETY: Secure gwAs in Federated Environment through a hYbrid Solution

  • FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs

  • Big data from electronic health records for early and late translational cardiovascular research: challenges and potential

  • Using big data to improve cardiovascular care and outcomes in China: a protocol for the CHinese Electronic health Records Research in Yinzhou (CHERRY) Study

  • Using nationwide ‘big data’ from linked electronic health records to help improve outcomes in cardiovascular diseases: 33 studies using methods from epidemiology, informatics, economics and social science in the ClinicAl disease research using LInked Bespoke studies and Electronic health Records (CALIBER) programme

  • Distributed clinical data sharing via dynamic access-control policy transformation

  • A secure EHR system based on hybrid clouds

  • A systematic literature review on security and privacy of electronic health record systems: technical perspectives

  • Security Techniques for the Electronic Health Records

  • Advances and current state of the security and privacy in electronic health records: survey from a social perspective

  • Assuring the privacy and security of transmitting sensitive electronic health information

  • Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions

  • Literature on Wearable Technology for Connected Health: Scoping Review of Research Trends, Advances, and Barriers

  • Privacy-preserving architecture for providing feedback to clinicians on their clinical performance

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

  • FedMed: A Federated Learning Framework for Language Modeling

  • Real-World Evidence Gathering in Oncology: The Need for a Biomedical Big Data Insight-Providing Federated Network

  • Federated queries of clinical data repositories: the sum of the parts does not equal the whole

  • FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery

  • Averaging Is Probably Not the Optimum Way of Aggregating Parameters in Federated Learning

  • LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

  • Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets

  • Implementing partnership-driven clinical federated electronic health record data sharing networks

  • Using a Federated Network of Real-World Data to Optimize Clinical Trials Operations

  • The project data sphere initiative: accelerating cancer research by sharing data

  • The national drug abuse treatment clinical trials network data share project: website design, usage, challenges, and future directions

  • A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens

  • Implementation of a deidentified federated data network for population-based cohort discovery

  • A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research

  • Federated Aggregate Cohort Estimator (FACE): an easy to deploy, vendor neutral, multi-institutional cohort query architecture

  • Sharing medical data for health research: the early personal health record experience

  • Patient-controlled sharing of medical imaging data across unaffiliated healthcare organizations

  • NeuroLOG: sharing neuroimaging data using an ontology-based federated approach

  • Multi-Objective Evolutionary Federated Learning

  • Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources

  • Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs

  • Variation-Aware Federated Learning with Multi-Source Decentralized Medical Image Data

  • Fold-stratified cross-validation for unbiased and privacy-preserving federated learning

  • Accounting for data variability in multi-institutional distributed deep learning for medical imaging

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

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

  • Federated Learning on Clinical Benchmark Data: Performance Assessment

  • A Secure Federated Transfer Learning Framework

  • TAG: Transformer Attack from Gradient

  • A BETTER ALTERNATIVE TO ERROR FEEDBACK FOR COMMUNICATION-EFFICIENT DISTRIBUTED LEARNING

  • Timely Communication in Federated Learning

  • FLBench: A Benchmark Suite for Federated Learning

  • FedMood:Federated Learning on Mobile Health Data for Mood Detection

  • FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

  • Advances and Open Problems in Federated Learning

  • Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning

  • PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization

  • Federated Transfer Learning: concept and applications

  • FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data

  • FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation

  • A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

  • Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers

  • Adversarial training in communication constrained federated learning

  • Distributionally Robust Federated Averaging

  • ESTIMATION OF CONTINUOUS BLOOD PRESSURE FROM PPG VIA A FEDERATED LEARNING APPROACH

  • Free-rider Attacks on Model Aggregation in Federated Learning

  • Federated Unlearning

  • SCALING NEUROSCIENCE RESEARCH USING FEDERATED LEARNING

  • Provably Secure Federated Learning against Malicious Clients

  • Hybrid Federated and Centralized Learning

  • A FIRST LOOK INTO THE CARBON FOOTPRINT OF FEDERATED LEARNING

  • Robust Federated Learning with Attack-Adaptive Aggregation

  • FLOP: Federated Learning on Medical Datasets using Partial Networks

  • Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation

  • Security and Privacy for Artificial Intelligence: Opportunities and Challenges

  • Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach

  • Decentralized Federated Learning Preserves Model and Data Privacy

  • Dopamine: Differentially Private Federated Learning on Medical Data

  • Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy

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

  • Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary

  • The Future of Digital Health with Federated Learning

  • Federated Learning: Opportunities and Challenges

  • Fusion of Federated Learning and Industrial Internet of Things: A Survey

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

  • Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention

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

  • Privacy-preserving medical image analysis

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

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

  • Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare

  • SAFER: Sparse Secure Aggregation for Federated Learning

  • Federated Learning for Healthcare Informatics

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

  • A Federated Learning Framework for Healthcare IoT devices

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

  • Evaluating the Communication Efficiency in Federated Learning Algorithms

  • FOCUS: Dealing with Label Quality Disparity in Federated Learning

  • The Disruptions of 5G on Data-driven Technologies and Applications

  • Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning

  • A blockchain-orchestrated Federated Learning architecture for healthcare consortia

  • FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

  • A Federated Filtering Framework for Internet of Medical Things

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

  • Facing small and biased data dilemma in drug discovery with federated learning

  • FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery

  • Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption

  • Reliable and automatic epilepsy classification with affordable, consumer-grade electroencephalography in rural sub-Saharan Afric

  • sPLINK: A Federated, Privacy-Preserving Tool as a Robust Alternative to Meta-Analysis in Genome-Wide Association Studies

  • Blockchained On-Device Federated Learning

  • Federated learning of predictive models from federated Electronic Health Records

  • Federated Learning with Non-IID Data

  • Federated Multi-Task Learning

  • Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data

  • Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records

  • Predictive Modeling of the Hospital Readmission Risk from Patients’ Claims Data Using Machine Learning: A Case Study on COPD

  • Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality

  • Deep learning for healthcare: review, opportunities and challenges

  • Differential Privacy-enabled Federated Learning for Sensitive Health Data

  • Dissecting racial bias in an algorithm used to manage the health of populations

  • Distributed learning from multiple EHR databases: Contextual embedding models for medical events

  • Federated and Differentially Private Learning for Electronic Health Records

  • Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

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

  • FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

  • Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation

    • [Paper]

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

  • LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

  • Modern Framework for Distributed Healthcare Data Analytics Based on Hadoop

  • National Health Information Privacy Regulations Under the Health Insurance Portability and Accountability Act

  • Split learning for health: Distributed deep learning without sharing raw patient data

  • Threats to Federated Learning: A Survey

  • Two-stage Federated Phenotyping and Patient Representation Learning

  • TOWARDS FEDERATED LEARNING AT SCALE: SYSTEM DESIGN

  • A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective

  • Privacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring

  • A Federated Learning Framework for Healthcare IoT devices

  • A Systematic Literature Review on Federated Learning: From A Model Quality Perspective

  • Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions

  • Artificial intelligence in COVID-19 drug repurposing

  • Understanding the nature of information seeking behavior in critical care: Implications for the design of health information technology

  • COMMUNICATION-COMPUTATION EFFICIENT SECURE AGGREGATION FOR FEDERATED LEARNING

  • Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review

  • Differential Privacy Protection Against Membership Inference Attack on Machine Learning for Genomic Data

  • Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

  • Molecular property prediction: recent trends in the era of artificial intelligence

  • Multimodal Privacy-preserving Mood Prediction from Mobile Data: A Preliminary Study

  • Computation-efficient Deep Model Training for Ciphertext-based Cross-silo Federated Learning

  • Privacy-preserving Artificial Intelligence Techniques in Biomedicine

  • Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare

  • Molecula rproperty prediction: recent trends in the era of artificial intelligence

  • A Framework for Edge-Assisted Healthcare Data Analytics using Federated Learning

  • A blockchain-orchestrated Federated Learning architecture for healthcare consortia

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

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

  • VAFL: a Method of Vertical Asynchronous Federated Learning

  • Anonymizing Data for Privacy-Preserving Federated Learning

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

  • Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

  • Modelling Audiological Preferences using Federated Learning

  • Privacy-first health research with federated learning

  • A Syntactic Approach for Privacy-Preserving Federated Learning

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

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

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

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

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

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

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

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

  • Personalized Federated Deep Learning for Pain Estimation From Face Images

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

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

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

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

  • Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics

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

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

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

  • Towards a Keyword Extraction in Medical and Healthcare Education

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

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

  • Federated Learning in Mobile Edge Networks: A Comprehensive Survey

  • DBA: Distributed Backdoor Attacks against Federated Learning

  • Three Approaches for Personalization with Applications to Federated Learning

  • Federated Learning of a Mixture of Global and Local Models

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

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

  • A Framework for Evaluating Gradient Leakage Attacks in Federated Learning

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

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

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

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

  • Multi-Center Federated Learning

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

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

Datasets

  • Federated Learning framework to preserve privacy

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A curated list of Federated Learning papers/articles and recent advancements.

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