Federated Learning for Healthcare Informatics

Abstract

With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, "big data". 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. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges and privacy issues in federated learning, and point out the implications and potentials in healthcare.

18 pages

References in corpus (26)

• Distilling the Knowledge in a Neural Network
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Towards Federated Learning at Scale: System Design
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• Towards Accurate Binary Convolutional Neural Network
• Improving Federated Learning Personalization via Model Agnostic Meta Learning
• Agnostic Federated Learning
• Threats to Federated Learning: A Survey
• BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
• Federated Learning for Emoji Prediction in a Mobile Keyboard
• Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• Peer-to-peer Federated Learning on Graphs
• One-Shot Federated Learning
• Federated Learning Of Out-Of-Vocabulary Words
• Central Server Free Federated Learning over Single-sided Trust Social Networks
• Federated and Differentially Private Learning for Electronic Health Records
• Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
• Decentralized Bayesian Learning over Graphs
• Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
• Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality
• Interpret Federated Learning with Shapley Values
• Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
• Robust Federated Training via Collaborative Machine Teaching using Trusted Instances
• Two-stage Federated Phenotyping and Patient Representation Learning
• Federated AI lets a team imagine together: Federated Learning of GANs

Cited by in corpus (14)

• Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review
• Privacy-preserving Artificial Intelligence Techniques in Biomedicine
• Privacy-Preserving Machine Learning: Methods, Challenges and Directions
• A Federated Learning Framework for Healthcare IoT devices
• Communication-Computation Efficient Secure Aggregation for Federated Learning
• Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources
• Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
• Attention on Personalized Clinical Decision Support System: Federated Learning Approach
• Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
• Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
• FedXGBoost: Privacy-Preserving XGBoost for Federated Learning
• Adapting deep generative approaches for getting synthetic data with realistic marginal distributions
• Distribution-Free Federated Learning with Conformal Predictions
• Strong Privacy and Utility Guarantee: Over-the-Air Statistical Estimation