Federated Learning for Smart Healthcare: A Survey

2021/11/16 by Dinh C. Nguyen, Quoc‐Viet Pham, Nguyen, Dinh C. +13 · 18 citations
Computer Science · Social Sciences · #Cryptography and Data Security #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2111.08834

openalex publication_date 2021/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Towards Federated Learning at Scale: System Design
• Differential Privacy-enabled Federated Learning for Sensitive Health Data
• Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving
• Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• Federated Learning With Differential Privacy: Algorithms and Performance Analysis
• Federated Learning on Non-IID Data Silos: An Experimental Study

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• Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach
• ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning
• MHARFedLLM: Multimodal Human Activity Recognition Using Federated Large Language Model
• Hypernetworks for Model-Heterogeneous Personalized Federated Learning
• Debunking Optimization Myths in Federated Learning for Medical Image Classification

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