Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction

• URL: http://arxiv.org/abs/2605.21563v1
• Date: Wed, 20 May 2026 16:31:53 GMT
• Title: Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction
• Abstract summary: We developed an embedding-based FL pipeline for iron deficiency prediction from routine full blood count data.<n>We deployed it across real institutional environments at Amsterdam University Medical Centre (AUMC) and NHS Blood and Transplant (NHSBT)<n>A frozen domain-specific haematology foundation model, DeepCBC, performs site-local representation extraction.
• Score: 1.690444853538344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent reviews find that the vast majority of published healthcare federated learning (FL) studies never reach real-world deployment. We developed an embedding-based FL pipeline for iron deficiency prediction from routine full blood count (FBC) data and deployed it across real institutional environments at Amsterdam University Medical Centre (AUMC) and NHS Blood and Transplant (NHSBT), two clinical environments that differ markedly in iron deficiency prevalence, ferritin distribution, and subject populations. A frozen domain-specific haematology foundation model, DeepCBC, performs site-local representation extraction, restricting federated training to a compact downstream classifier and substantially reducing recurrent communication relative to full-encoder federation. The two clinical datasets are structurally not independent and identically distributed (non-IID), with heterogeneity arising from distinct population differences rather than sampling artefacts. Runtime governance is enforced by FLA$^3$, a healthcare-oriented FL platform providing study-scoped execution, policy-based authorisation, and signed audit logging. Standard sample-size-weighted aggregation (FedAvg) reduced the area under the receiver operating characteristic curve (ROC-AUC) at both sites relative to local-only training, as the global update was biased towards the larger AUMC distribution. FedMAP, a personalised aggregation method, raised ROC-AUC from 0.9470 to 0.9594 at AUMC and from 0.8558 to 0.8671 at NHSBT relative to local-only training, achieving the highest macro ROC-AUC of 0.9133 and the best macro balanced accuracy overall. These results support personalised aggregation in clinical federations where client sample size and task relevance diverge substantially.

Related papers

• Federated LoRA Adaptation of BiomedCLIP Across Four International Chest X-Ray Cohorts [3.8298947210288374]
  Federated learning lets institutions train a shared model without exchanging data.<n>Low-Rank Adaptation (LoRA) makes this practical at scale by communicating only compact low-rank updates.
  arXiv  Detail & Related papers  (2026-09-02T04:40:05Z)
• FedPref: Federated Preference Learning for Structured Radiology Report Extraction [69.11739400975445]
  Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema.<n>We introduce FedPref: frozen public language models propose alternative extractions, local annotations rank them, and sites collaboratively train pooled compact Qwen3-8B adapters.<n>On development data from six simulated hospitals with unequal data volume and disease prevalence, FedPref improves client-mean F1 by 2.49 points and worst-site F1 by 9.10 points compared with training each site in isolation.
  arXiv  Detail & Related papers  (2026-08-17T12:29:42Z)
• Evaluating Federated Learning approaches for mammography under breast density heterogeneity [0.0]
  Breast density is a key factor that influences mammography interpretation.<n>This study aims to evaluate the impact of breast density-induced heterogeneity on FL for mammography image classification.
  arXiv  Detail & Related papers  (2026-05-09T19:44:16Z)
• Deep Learning-based Assessment of the Relation Between the Third Molar and Mandibular Canal on Panoramic Radiographs using Local, Centralized, and Federated Learning [0.3186329231961084]
  Impaction of the mandibular third molar in proximity to the mandibular canal increases the risk of inferior alveolar nerve injury.<n>Automated classification of molar-canal overlap could support clinical triage and reduce unnecessary CBCT referrals.<n>Federated learning enables multi-center collaboration without sharing patient data.
  arXiv  Detail & Related papers  (2026-03-12T12:17:17Z)
• A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine [59.78991974851707]
  Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis.<n>Most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems.<n>We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications.
  arXiv  Detail & Related papers  (2026-01-29T18:48:21Z)
• FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging [12.307490659840845]
  We introduce FedMRL, a novel multi-agent deep reinforcement learning framework designed to address data heterogeneity. FedMRL incorporates a novel loss function to facilitate fairness among clients, preventing bias in the final global model. We assess our approach using two publicly available real-world medical datasets, and the results demonstrate that FedMRL significantly outperforms state-of-the-art techniques.
  arXiv  Detail & Related papers  (2024-07-08T10:10:07Z)
• FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems [14.378330471182586]
  Federated learning (FL) promises to enable machine learning across healthcare sites whilst preserving data privacy.<n>We introduce FedMAP, a personalised FL framework that addresses statistical heterogeneity through local Maximum a Posteriori (MAP) estimation.<n>We demonstrate improved performance compared to local training, FedAvg, and several PFL methods across three large-scale clinical datasets.
  arXiv  Detail & Related papers  (2024-05-29T11:28:06Z)
• Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping [64.58402571292723]
  We propose a manifold reshaping approach called FedMR to calibrate the feature space of local training. We conduct extensive experiments on a range of datasets to demonstrate that our FedMR achieves much higher accuracy and better communication efficiency.
  arXiv  Detail & Related papers  (2024-05-29T10:56:13Z)
• Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts [11.562953837452126]
  We make the first attempt to assess the informativeness of local data derived from diverse domains. We propose a novel methodology termed Federated Evidential Active Learning (FEAL) to calibrate the data evaluation under domain shift.
  arXiv  Detail & Related papers  (2023-12-05T08:32:27Z)
• A Federated Learning Framework for Stenosis Detection [70.27581181445329]
  This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA) Two heterogeneous datasets from two institutions were considered: dataset 1 includes 1219 images from 200 patients, which we acquired at the Ospedale Riuniti of Ancona (Italy) dataset 2 includes 7492 sequential images from 90 patients from a previous study available in the literature.
  arXiv  Detail & Related papers  (2023-10-30T11:13:40Z)
• Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning [60.41501515192088]
  Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively. The data samples usually follow a long-tailed distribution in the real world, and FL on the decentralized and long-tailed data yields a poorly-behaved global model. In this work, we integrate the local real data with the global gradient prototypes to form the local balanced datasets.
  arXiv  Detail & Related papers  (2023-01-25T03:18:10Z)
• Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance [65.61909544178603]
  We study a practical yet challenging problem of class imbalanced semi-supervised FL (imFed-Semi) This imFed-Semi problem is addressed by a novel dynamic bank learning scheme, which improves client training by exploiting class proportion information. We evaluate our approach on two public real-world medical datasets, including the intracranial hemorrhage diagnosis with 25,000 CT slices and skin lesion diagnosis with 10,015 dermoscopy images.
  arXiv  Detail & Related papers  (2022-06-27T06:51:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.