# From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare

arXiv: https://arxiv.org/abs/2409.09727 · HTML: https://mlsanity.com/paper/2409.09727/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 85 of its 196 references that are in the map.
- First posted on arXiv 15 Sep 2024.

## Builds on (most cited first, 30 of 85)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Learning Structured Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1608.03665/) (2016)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [FetchSGD: Communication-Efficient Federated Learning with Sketching](https://mlsanity.com/paper/2007.07682/) (2020)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://mlsanity.com/paper/2105.11367/) (2021)
- [One-Shot Federated Learning](https://mlsanity.com/paper/1902.11175/) (2019)
- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [Stochastic Controlled Averaging for Federated Learning with Communication Compression](https://mlsanity.com/paper/2308.08165/) (2023)
- [NVIDIA FLARE: Federated Learning from Simulation to Real-World](https://mlsanity.com/paper/2210.13291/) (2022)
- [On the Importance and Applicability of Pre-Training for Federated Learning](https://mlsanity.com/paper/2206.11488/) (2022)
- [Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning](https://mlsanity.com/paper/2210.08090/) (2022)
- [A Review of Challenges and Opportunities in Machine Learning for Health](https://mlsanity.com/paper/1806.00388/) (2018)
- [When Machine Learning Meets Privacy: A Survey and Outlook](https://mlsanity.com/paper/2011.11819/) (2020)

## Built on by (most cited first, 16 of 16)

- [Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI](https://mlsanity.com/paper/2505.22108/) (2025)
- [A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs](https://mlsanity.com/paper/2603.15651/) (2026)
- [Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction](https://mlsanity.com/paper/2605.21563/) (2026)
- [GEN-Guard: Correcting Generalization Failures for Deployable Federated Surgical AI](https://mlsanity.com/paper/2606.20303/) (2026)
- [Batch effects can impair federated learning in multi-center omics studies](https://mlsanity.com/paper/2412.05894/) (2024)
- [Adapting Natural Language Processing Models Across Jurisdictions: A pilot Study in Canadian Cancer Registries](https://mlsanity.com/paper/2601.00787/) (2026)
- [Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learning](https://mlsanity.com/paper/2601.22669/) (2026)
- [Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning](https://mlsanity.com/paper/2602.17625/) (2026)
- [FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentation](https://mlsanity.com/paper/2603.11659/) (2026)
- [Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosis](https://mlsanity.com/paper/2604.05649/) (2026)
- [Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction](https://mlsanity.com/paper/2607.08595/) (2026)
- [Task-Aware Federated Fine-Tuning for MoE-based Large Language Models](https://mlsanity.com/paper/2609.13395/) (2026)
- [Adaptive Bayesian Partner Selection for Federated Clinical Centers](https://mlsanity.com/paper/2609.16446/) (2026)
- [Joint Domain-Class Modeling for Federated Learning Under Feature Skew](https://mlsanity.com/paper/2609.22932/) (2026)
- [A generalizable structural brain MRI foundation model built through dual-priority federated pretraining](https://mlsanity.com/paper/2609.27611/) (2026)
- [Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study](https://mlsanity.com/paper/2609.36001/) (2026)

## Abstract (arXiv)

Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning based methods in healthcare, it remains unclear which ones have potential clinical utility. This review paper considers and analyzes the most recent studies up to May 2024 that describe federated learning based methods in healthcare. After a thorough review, we find that the vast majority are not appropriate for clinical use due to their methodological flaws and/or underlying biases which include but are not limited to privacy concerns, generalization issues, and communication costs. As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that might be implemented to resolve these problems and improve the quality of model development in federated learning with healthcare.