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.
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!
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Tensorflow Federated
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An Industrial Grade Federated Learning Framework
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Flower - A Friendly Federated Learning Framework
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Data science on data without acquiring a copy
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Federated learning for healthcare informatics
- Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter Walker, Jiang Bian, Fei Wang
- [Paper]
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The future of digital health with federated learning
- Nicola Rieke
- [Paper]
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Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges
- Madhura Joshi , Ankit Pal , Malaikannan Sankarasubbu
- [Paper]
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Federated Learning for Healthcare Informatics1
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AI in Health: State of the Art, Challenges, and Future Directions
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Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges
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Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care
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Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis
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Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19
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Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan
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Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
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The value of federated learning during and post-COVID-19
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SCOR: A secure international informatics infrastructure to investigate COVID-19
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Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study
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COVID-19 IMAGING DATA PRIVACY BY FEDERATED LEARNING DESIGN: A THEORETICAL FRAMEWORK
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Artificial intelligence in COVID-19 drug repurposing
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Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging
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Experiments of Federated Learning for COVID-19 Chest X-ray Images
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Federated Learning on Clinical Benchmark Data: Performance Assessment
- Geun Hyeong Lee and Soo-Yong Shin
- [Paper]
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Secure and Robust Machine Learning for Health Care
- Adnan Qayyum, Junaid Qadir, Muhammad Bilal, Ala Al-Fuqaha
- [Paper]
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Privacy-first health research with federated learning
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Patch-Based Surface Morphometry Feature Selection with Federated Group Lasso Regression
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Predicting Adverse Drug Reactions on Distributed Health Data using Federated Learning
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Federated electronic health records research technology to support clinical trial protocol optimization: Evidence from EHR4CR and the InSite platform
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Probabilistic Predictions with Federated Learning
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Using federated data sources and Varian Learning Portal framework to train a neural network model for automatic organ segmentation
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Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices
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Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning
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Privacy-preserving model learning on a blockchain network-of-networks
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Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept
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Healthchain: A novel framework on privacy preservation of electronic health records using blockchain technology
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Privacy-Preserving in Healthcare Blockchain Systems Based on Lightweight Message Sharing
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MedBlock: Efficient and Secure Medical Data Sharing Via Blockchain
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Blockchain distributed ledger technologies for biomedical and health care applications
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A Decentralized Privacy-Preserving Healthcare Blockchain for IoT
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Joint Imaging Platform for Federated Clinical Data Analytics
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Federated Transfer Learning for EEG Signal Classification
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Federated Learning used for predicting outcomes in SARS-COV-2 patients
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Large-Scale Water Quality Prediction Using Federated Sensing and Learning: A Case Study with Real-World Sensing Big-Data
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Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
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Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
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Security and privacy requirements for a multi-institutional cancer research data grid: an interview-based study
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Federated learning of predictive models from federated Electronic Health Records
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Creating a data exchange strategy for radiotherapy research: towards federated databases and anonymised public datasets
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Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation
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Balancing Accuracy and Privacy in Federated Queries of Clinical Data Repositories: Algorithm Development and Validation
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A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning
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Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation
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A review on the state-of-the-art privacy-preserving approaches in the e-health clouds
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eHealth Cloud Security Challenges: A Survey
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Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT
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Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries
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Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept
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How Should Health Data Be Used?
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Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes
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Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling
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A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification
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Multi-center machine learning in imaging psychiatry: A meta-model approach
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A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies
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Distributed deep learning networks among institutions for medical imaging
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The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm
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WebDISCO: a web service for distributed cox model learning without patient-level data sharing
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Differentially Private Distributed Online Learning
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An Uplink Communication-Efficient Approach to Featurewise Distributed Sparse Optimization With Differential Privacy
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A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy
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Secure Multiparty Quantum Computation for Summation and Multiplication
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Hybrid Quantum Protocols for Secure Multiparty Summation and Multiplication
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A blockchain-based scheme for privacy-preserving and secure sharing of medical data
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Cost-Efficient and Multi-Functional Secure Aggregation in Large Scale Distributed Application
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Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees
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Security issues in healthcare applications using wireless medical sensor networks: a survey
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A secure distributed logistic regression protocol for the detection of rare adverse drug events
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High performance logistic regression for privacy-preserving genome analysis
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Efficient Privacy-Preserving Access Control Scheme in Electronic Health Records System
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Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory
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Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm
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DPSynthesizer: Differentially Private Data Synthesizer for Privacy Preserving Data Sharing
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A flexible approach to distributed data anonymization
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Privacy-preserving data cube for electronic medical records: An experimental evaluation
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A framework to preserve the privacy of electronic health data streams
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Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation
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Design and implementation of a privacy preserving electronic health record linkage tool in Chicago
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Privacy preserving interactive record linkage (PPIRL)
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Privacy-preserving record linkage in large databases using secure multiparty computation
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Sample Complexity Bounds for Differentially Private Learning
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Convergence Rates for Differentially Private Statistical Estimation
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Efficient differentially private learning improves drug sensitivity prediction
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A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis
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Privacy-preserving aggregation of personal health data streams
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Efficient and Privacy-Preserving Online Medical Prediagnosis Framework Using Nonlinear SVM
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Privacy-preserving biomedical data dissemination via a hybrid approach
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A community effort to protect genomic data sharing, collaboration and outsourcing
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Privacy challenges and research opportunities for genomic data sharing
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Privacy-Preserving Integration of Medical Data : A Practical Multiparty Private Set Intersection
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Secure multiparty computation for privacy-preserving drug discovery
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Privacy-Preserving Cost-Sensitive Learning
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Differentially Private Empirical Risk Minimization
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Privacy-preserving heterogeneous health data sharing
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A comprehensive tool for creating and evaluating privacy-preserving biomedical prediction models
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Privacy-enhancing ETL-processes for biomedical data
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Privacy-preserving restricted boltzmann machine
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Privacy preserving processing of genomic data: A survey
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How (not) to protect genomic data privacy in a distributed network: using trail re-identification to evaluate and design anonymity protection systems
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Are privacy-enhancing technologies for genomic data ready for the clinic? A survey of medical experts of the Swiss HIV Cohort Study
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Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States
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The tension between data sharing and the protection of privacy in genomics research
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ConTPL: Controlling Temporal Privacy Leakage in Differentially Private Continuous Data Release
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New threats to health data privacy
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Securing electronic health records without impeding the flow of information
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How to Accurately and Privately Identify Anomalies
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A Guide for Private Outlier Analysis
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Privacy-Aware Distributed Hypothesis Testing
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Distributed Hypothesis Testing with Privacy Constraints
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Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing
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Web-Based Privacy-Preserving Multicenter Medical Data Analysis Tools Via Threshold Homomorphic Encryption: Design and Development Study
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A Privacy-Preserving Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition
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Privacy-enhanced multi-party deep learning
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Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications
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Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis
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A Critical Evaluation of Privacy and Security Threats in Federated Learning
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Properties of a federated epidemiology query system
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Advanced and secure architectural EHR approaches
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Implementing security in a distributed web-based EHCR
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Health information systems - past, present, future
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Federated healthcare record server--the Synapses paradigm
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The basic principles of the synapses federated healthcare record server
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Ternary Compression for Communication-Efficient Federated Learning
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Distributed learning on 20 000+ lung cancer patients - The Personal Health Train
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A Distributed Ensemble Approach for Mining Healthcare Data under Privacy Constraints
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FeARH: Federated machine learning with Anonymous Random Hybridization on electronic medical records
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Smart Medical Information Technology for Healthcare (SMITH)
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Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records
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Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
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Federated Tensor Factorization for Computational Phenotyping
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Cloud-Based Federated Learning Implementation Across Medical Centers
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ACTION-EHR: Patient-Centric Blockchain-Based Electronic Health Record Data Management for Cancer Care
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Federated learning improves site performance in multicenter deep learning without data sharing
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Healthcare information exchange system based on a hybrid central/federated model
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Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies
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Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation
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The future of digital health with federated learning
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A novel privacy-preserving federated genome-wide association study framework and its application in identifying potential risk variants in ankylosing spondylitis
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Privacy-preserving GWAS analysis on federated genomic datasets
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SAFETY: Secure gwAs in Federated Environment through a hYbrid Solution
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FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs
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Big data from electronic health records for early and late translational cardiovascular research: challenges and potential
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Using big data to improve cardiovascular care and outcomes in China: a protocol for the CHinese Electronic health Records Research in Yinzhou (CHERRY) Study
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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
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Distributed clinical data sharing via dynamic access-control policy transformation
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A secure EHR system based on hybrid clouds
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A systematic literature review on security and privacy of electronic health record systems: technical perspectives
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Security Techniques for the Electronic Health Records
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Advances and current state of the security and privacy in electronic health records: survey from a social perspective
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Assuring the privacy and security of transmitting sensitive electronic health information
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Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions
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Literature on Wearable Technology for Connected Health: Scoping Review of Research Trends, Advances, and Barriers
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Privacy-preserving architecture for providing feedback to clinicians on their clinical performance
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FedMed: A Federated Learning Framework for Language Modeling
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Real-World Evidence Gathering in Oncology: The Need for a Biomedical Big Data Insight-Providing Federated Network
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Federated queries of clinical data repositories: the sum of the parts does not equal the whole
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FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery
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Averaging Is Probably Not the Optimum Way of Aggregating Parameters in Federated Learning
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LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
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Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets
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Implementing partnership-driven clinical federated electronic health record data sharing networks
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Using a Federated Network of Real-World Data to Optimize Clinical Trials Operations
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The project data sphere initiative: accelerating cancer research by sharing data
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The national drug abuse treatment clinical trials network data share project: website design, usage, challenges, and future directions
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A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens
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Implementation of a deidentified federated data network for population-based cohort discovery
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A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research
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Federated Aggregate Cohort Estimator (FACE): an easy to deploy, vendor neutral, multi-institutional cohort query architecture
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Sharing medical data for health research: the early personal health record experience
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Patient-controlled sharing of medical imaging data across unaffiliated healthcare organizations
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NeuroLOG: sharing neuroimaging data using an ontology-based federated approach
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Multi-Objective Evolutionary Federated Learning
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Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources
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Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs
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Variation-Aware Federated Learning with Multi-Source Decentralized Medical Image Data
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Fold-stratified cross-validation for unbiased and privacy-preserving federated learning
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Accounting for data variability in multi-institutional distributed deep learning for medical imaging
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AI in Health: State of the Art, Challenges, and Future Directions
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Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care
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Federated Learning on Clinical Benchmark Data: Performance Assessment
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A Secure Federated Transfer Learning Framework
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TAG: Transformer Attack from Gradient
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A BETTER ALTERNATIVE TO ERROR FEEDBACK FOR COMMUNICATION-EFFICIENT DISTRIBUTED LEARNING
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Timely Communication in Federated Learning
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FLBench: A Benchmark Suite for Federated Learning
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FedMood:Federated Learning on Mobile Health Data for Mood Detection
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FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
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Advances and Open Problems in Federated Learning
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Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
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PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization
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Federated Transfer Learning: concept and applications
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FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
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FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation
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A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
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Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers
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Adversarial training in communication constrained federated learning
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Distributionally Robust Federated Averaging
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ESTIMATION OF CONTINUOUS BLOOD PRESSURE FROM PPG VIA A FEDERATED LEARNING APPROACH
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Free-rider Attacks on Model Aggregation in Federated Learning
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Federated Unlearning
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SCALING NEUROSCIENCE RESEARCH USING FEDERATED LEARNING
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Provably Secure Federated Learning against Malicious Clients
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Hybrid Federated and Centralized Learning
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A FIRST LOOK INTO THE CARBON FOOTPRINT OF FEDERATED LEARNING
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Robust Federated Learning with Attack-Adaptive Aggregation
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FLOP: Federated Learning on Medical Datasets using Partial Networks
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Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation
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Security and Privacy for Artificial Intelligence: Opportunities and Challenges
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Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach
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Decentralized Federated Learning Preserves Model and Data Privacy
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Dopamine: Differentially Private Federated Learning on Medical Data
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Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy
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Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary
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The Future of Digital Health with Federated Learning
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Federated Learning: Opportunities and Challenges
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Fusion of Federated Learning and Industrial Internet of Things: A Survey
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Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare
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Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
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FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
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Privacy-preserving medical image analysis
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Confederated learning in healthcare: training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale Health System Intelligence
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Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare
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SAFER: Sparse Secure Aggregation for Federated Learning
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Federated Learning for Healthcare Informatics
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A Federated Learning Framework for Privacy-preserving and Parallel Training
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A Federated Learning Framework for Healthcare IoT devices
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FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning
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Evaluating the Communication Efficiency in Federated Learning Algorithms
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FOCUS: Dealing with Label Quality Disparity in Federated Learning
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The Disruptions of 5G on Data-driven Technologies and Applications
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Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning
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A blockchain-orchestrated Federated Learning architecture for healthcare consortia
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FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
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A Federated Filtering Framework for Internet of Medical Things
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FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
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Facing small and biased data dilemma in drug discovery with federated learning
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FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery
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Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption
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Reliable and automatic epilepsy classification with affordable, consumer-grade electroencephalography in rural sub-Saharan Afric
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sPLINK: A Federated, Privacy-Preserving Tool as a Robust Alternative to Meta-Analysis in Genome-Wide Association Studies
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Blockchained On-Device Federated Learning
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Federated learning of predictive models from federated Electronic Health Records
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Federated Learning with Non-IID Data
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Federated Multi-Task Learning
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Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
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Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records
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Predictive Modeling of the Hospital Readmission Risk from Patients’ Claims Data Using Machine Learning: A Case Study on COPD
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Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality
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Deep learning for healthcare: review, opportunities and challenges
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Differential Privacy-enabled Federated Learning for Sensitive Health Data
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Dissecting racial bias in an algorithm used to manage the health of populations
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Distributed learning from multiple EHR databases: Contextual embedding models for medical events
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Federated and Differentially Private Learning for Electronic Health Records
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Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data
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FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
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Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
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Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm
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LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
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Modern Framework for Distributed Healthcare Data Analytics Based on Hadoop
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National Health Information Privacy Regulations Under the Health Insurance Portability and Accountability Act
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Split learning for health: Distributed deep learning without sharing raw patient data
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Threats to Federated Learning: A Survey
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Two-stage Federated Phenotyping and Patient Representation Learning
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TOWARDS FEDERATED LEARNING AT SCALE: SYSTEM DESIGN
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A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective
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Privacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring
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A Federated Learning Framework for Healthcare IoT devices
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A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
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Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions
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Understanding the nature of information seeking behavior in critical care: Implications for the design of health information technology
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COMMUNICATION-COMPUTATION EFFICIENT SECURE AGGREGATION FOR FEDERATED LEARNING
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Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review
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Differential Privacy Protection Against Membership Inference Attack on Machine Learning for Genomic Data
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Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
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Molecular property prediction: recent trends in the era of artificial intelligence
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Multimodal Privacy-preserving Mood Prediction from Mobile Data: A Preliminary Study
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Computation-efficient Deep Model Training for Ciphertext-based Cross-silo Federated Learning
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Privacy-preserving Artificial Intelligence Techniques in Biomedicine
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Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare
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Molecula rproperty prediction: recent trends in the era of artificial intelligence
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A Framework for Edge-Assisted Healthcare Data Analytics using Federated Learning
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A blockchain-orchestrated Federated Learning architecture for healthcare consortia
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A NOVEL APPROACH TO MACHINE LEARNING APPLICATION TO PROTECTION PRIVACY DATA IN HEALTHCARE: FEDERATED LEARNING
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FEEL: A Federated Edge Learning System for Efficient and Privacy-Preserving Mobile Healthcare
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VAFL: a Method of Vertical Asynchronous Federated Learning
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Anonymizing Data for Privacy-Preserving Federated Learning
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FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning
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Modelling Audiological Preferences using Federated Learning
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Privacy-first health research with federated learning
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A Syntactic Approach for Privacy-Preserving Federated Learning
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Achieving Privacy-preserving Federated Learning with Irrelevant Updates over E-Health Applications
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FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
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A Federated Learning Framework for Privacy-preserving and Parallel Training
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Attack Detection Using Federated Learning in Medical Cyber-Physical Systems
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Dealing with Open Issues and Unmet Needs in Healthcare Through Ontology Matching and Federated Learning
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Federated Learning used for predicting outcomes in SARS-COV-2 patients
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FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
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Personalized Federated Deep Learning for Pain Estimation From Face Images
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Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare
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Reproduce Distributed Learning Networks for Medical Imaging and Investigate the Performance in Edge Scenarios (Healthcare)
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DNet: An Efficient Privacy-Preserving Distributed Learning Framework for Healthcare Systems
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A pseudonymisation protocol with implicit and explicit consent routes for health records in federated ledgers
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Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics
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A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services
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The Evolution of a Healthcare Software Framework: Reuse, Evaluation and Lessons Learned
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Confederated learning in healthcare: training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale Health System Intelligence
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Towards a Keyword Extraction in Medical and Healthcare Education
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From the Data on Many, Precision Medicine for “One”: The Case for Widespread Genomic Data Sharing
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Federated Learning in Mobile Edge Networks: A Comprehensive Survey
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DBA: Distributed Backdoor Attacks against Federated Learning
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Three Approaches for Personalization with Applications to Federated Learning
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Federated Learning of a Mixture of Global and Local Models
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Think Locally, Act Globally: Federated Learning with Local and Global Representations
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Inverting Gradients - How easy is it to break privacy in federated learning?
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A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
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Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
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Federated learning in medicine: facilitating multi‑institutional collaborations without sharing patient data
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Multi-Center Federated Learning
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Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
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VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange
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One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis
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Federated Learning of Generative Image Priors for MRI Reconstruction
- Federated Learning framework to preserve privacy
