mlsanityData to 7 Oct 2026

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

arXiv 2409.09727 ↗First posted 15 Sep 2024cs.LG cs.AIcited by 16

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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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 85 of its 196 references that are in the map. First posted on arXiv 15 Sep 2024.

Builds on

85 of 196 references.

References · 85

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38 more
  1. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  2. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 351
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 14.

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  1. 2025Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AIcited by 1
  2. 2026A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUscited by 1
  3. 2026Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Predictioncited by 1
  4. 2026GEN-Guard: Correcting Generalization Failures for Deployable Federated Surgical AIcited by 1
  5. 2024Batch effects can impair federated learning in multi-center omics studiescited by 0
  6. 2026Adapting Natural Language Processing Models Across Jurisdictions: A pilot Study in Canadian Cancer Registriescited by 0
  7. 2026Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learningcited by 0
  8. 2026Catastrophic Forgetting Resilient One-Shot Incremental Federated Learningcited by 0
  9. 2026FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentationcited by 0
  10. 2026Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosiscited by 0
  11. 2026Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Predictioncited by 0
  12. 2026Task-Aware Federated Fine-Tuning for MoE-based Large Language Modelscited by 0
4 more
  1. 2026Adaptive Bayesian Partner Selection for Federated Clinical Centerscited by 0
  2. 2026Joint Domain-Class Modeling for Federated Learning Under Feature Skewcited by 0
  3. 2026A generalizable structural brain MRI foundation model built through dual-priority federated pretrainingcited by 0
  4. 2026Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Studycited by 0