mlsanityData to 7 Oct 2026

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

arXiv 2208.03392 ↗First posted 5 Aug 2022cs.LG cs.CR cs.CVcited by 34

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Abstract (arXiv)

With the advent of the IoT, AI, ML, and DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in acFL have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable acFL models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of acFL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 93 of its 287 references that are in the map. First posted on arXiv 5 Aug 2022.

Builds on

93 of 287 references.

References · 93

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38 more
  1. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 13; 2025: 14; 2026: 4.

34 papers.

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  1. 2024FedFMS: Exploring Federated Foundation Models for Medical Image Segmentationcited by 21
  2. 2024Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learningcited by 8
  3. 2024Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Frameworkcited by 6
  4. 2024A Generalist Model for Diverse Text-Guided Medical Image Synthesiscited by 6
  5. 2024Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gaincited by 5
  6. 2025FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Healthcited by 5
  7. 2024Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosiscited by 4
  8. 2024A survey on secure decentralized optimization and learningcited by 4
  9. 2024EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learningcited by 4
  10. 2025Revisit the Stability of Vanilla Federated Learning Under Diverse Conditionscited by 4
  11. 2023FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classificationcited by 3
  12. 2024Enhancing Privacy in Federated Learning through Local Trainingcited by 3
22 more
  1. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  2. 2025Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Datacited by 3
  3. 2025Debunking Optimization Myths in Federated Learning for Medical Image Classificationcited by 3
  4. 2023WW-FL: Secure and Private Large-Scale Federated Learningcited by 2
  5. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 2
  6. 2025ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuningcited by 1
  7. 2025Hi-SAFE: Hierarchical Secure Aggregation for Lightweight Federated Learningcited by 1
  8. 2023Cognizance of Post-COVID-19 Multi-Organ Dysfunction through Machine Learning Analysiscited by 0
  9. 2024Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic reviewcited by 0
  10. 2024BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networkscited by 0
  11. 2024Batch effects can impair federated learning in multi-center omics studiescited by 0
  12. 2025Privacy-Preserving Quantized Federated Learning with Diverse Precisioncited by 0
  13. 2025VGS-ATD: Robust Distributed Learning for Multi-Label Medical Image Classification Under Heterogeneous and Imbalanced Conditionscited by 0
  14. 2025Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systemscited by 0
  15. 2025FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimationcited by 0
  16. 2025A Service-Oriented Adaptive Hierarchical Incentive Mechanism for Federated Learningcited by 0
  17. 2025Deep Learning Approaches with Explainable AI for Differentiating Alzheimer Disease and Mild Cognitive Impairmentcited by 0
  18. 2025Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Researchcited by 0
  19. 2026Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learningcited by 0
  20. 2026Social Hippocampus Memory Learningcited by 0
  21. 2026AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learningcited by 0
  22. 2026FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filteringcited by 0