Federated Learning for Smart Healthcare: A Survey
arXiv:2111.08834
Abstract
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.
Accepted at ACM Computing Surveys, 35 pages
References in corpus (30)
- GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
- Federated Learning with Non-IID Data
- Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
- Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
- Towards Federated Learning at Scale: System Design
- Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts
- FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
- Multi-Armed Bandit Based Client Scheduling for Federated Learning
- Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey
- Practical Secure Aggregation for Federated Learning on User-Held Data
- BEdgeHealth: A Decentralized Architecture for Edge-based IoMT Networks Using Blockchain
- Differential Privacy-enabled Federated Learning for Sensitive Health Data
- Split learning for health: Distributed deep learning without sharing raw patient data
- Decentralized Federated Learning: A Segmented Gossip Approach
- Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
- Federated Learning on Non-IID Data Silos: An Experimental Study
- Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging
- FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
- Experiments of Federated Learning for COVID-19 Chest X-ray Images
- Federated and Differentially Private Learning for Electronic Health Records
- Dopamine: Differentially Private Federated Learning on Medical Data
- Personalized Federated Deep Learning for Pain Estimation From Face Images
- Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
- Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning
- CaPC Learning: Confidential and Private Collaborative Learning
- Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving
- Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence
- Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks
- Resource-Constrained Federated Learning with Heterogeneous Labels and Models
- Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring