# Federated Learning for Smart Healthcare: A Survey

arXiv: https://arxiv.org/abs/2111.08834 · HTML: https://mlsanity.com/paper/2111.08834/

## Key facts

- Cited by 184 papers in the map (data through 14 Oct 2026).
- Builds on 73 of its 121 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 30 of 73)

- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [Machine Learning with Membership Privacy using Adversarial Regularization](https://mlsanity.com/paper/1807.05852/) (2018)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [Privacy-preserving Federated Brain Tumour Segmentation](https://mlsanity.com/paper/1910.00962/) (2019)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results](https://mlsanity.com/paper/2001.05647/) (2020)
- [One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis](https://mlsanity.com/paper/2001.05713/) (2020)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning](https://mlsanity.com/paper/2007.07174/) (2020)
- [Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning](https://mlsanity.com/paper/2002.04156/) (2020)
- [Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning](https://mlsanity.com/paper/2103.02148/) (2021)
- [Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges](https://mlsanity.com/paper/2104.01776/) (2021)

## Built on by (most cited first, 30 of 184)

- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [Towards Efficient Replay in Federated Incremental Learning](https://mlsanity.com/paper/2403.05890/) (2024)
- [Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2203.09702/) (2022)
- [Membership Inference Attacks and Defenses in Federated Learning: A Survey](https://mlsanity.com/paper/2412.06157/) (2024)
- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning](https://mlsanity.com/paper/2409.01128/) (2024)
- [Faster federated optimization under second-order similarity](https://mlsanity.com/paper/2209.02257/) (2022)
- [FedICT: Federated Multi-task Distillation for Multi-access Edge Computing](https://mlsanity.com/paper/2301.00389/) (2023)
- [Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods](https://mlsanity.com/paper/2305.11386/) (2023)
- [FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning](https://mlsanity.com/paper/2307.13214/) (2023)
- [FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology](https://mlsanity.com/paper/2402.15858/) (2024)
- [A Survey for Federated Learning Evaluations: Goals and Measures](https://mlsanity.com/paper/2308.11841/) (2023)
- [A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations](https://mlsanity.com/paper/2406.10303/) (2024)
- [Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis](https://mlsanity.com/paper/2311.02402/) (2023)
- [AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices](https://mlsanity.com/paper/2312.10935/) (2023)
- [Federated Distillation: A Survey](https://mlsanity.com/paper/2404.08564/) (2024)
- [Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation](https://mlsanity.com/paper/2409.01435/) (2024)
- [Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence](https://mlsanity.com/paper/2412.13779/) (2024)
- [Remote patient monitoring using artificial intelligence: Current state, applications, and challenges](https://mlsanity.com/paper/2301.10009/) (2023)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning](https://mlsanity.com/paper/2309.10283/) (2023)
- [FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels](https://mlsanity.com/paper/2312.12263/) (2023)
- [Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection](https://mlsanity.com/paper/2503.07978/) (2025)
- [Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning](https://mlsanity.com/paper/2305.00312/) (2023)

## Abstract (arXiv)

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.