Federated Few-Shot Learning for Epileptic Seizure Detection Under Privacy Constraints

• URL: http://arxiv.org/abs/2512.13717v1
• Date: Tue, 09 Dec 2025 16:01:35 GMT
• Title: Federated Few-Shot Learning for Epileptic Seizure Detection Under Privacy Constraints
• Abstract summary: We propose a two-stage federated few-shot learning framework for personalized EEG-based seizure detection.<n>In Stage 1, a pretrained biosignal transformer (BIOT) is fine-tuned across non-IID simulated hospital sites using federated learning.<n>In Stage 2, federated few-shot personalization adapts the classifier to each patient using only five labeled EEG segments.
• Score: 1.4150452759904846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many deep learning approaches have been developed for EEG-based seizure detection; however, most rely on access to large centralized annotated datasets. In clinical practice, EEG data are often scarce, patient-specific distributed across institutions, and governed by strict privacy regulations that prohibit data pooling. As a result, creating usable AI-based seizure detection models remains challenging in real-world medical settings. To address these constraints, we propose a two-stage federated few-shot learning (FFSL) framework for personalized EEG-based seizure detection. The method is trained and evaluated on the TUH Event Corpus, which includes six EEG event classes. In Stage 1, a pretrained biosignal transformer (BIOT) is fine-tuned across non-IID simulated hospital sites using federated learning, enabling shared representation learning without centralizing EEG recordings. In Stage 2, federated few-shot personalization adapts the classifier to each patient using only five labeled EEG segments, retaining seizure-specific information while still benefiting from cross-site knowledge. Federated fine-tuning achieved a balanced accuracy of 0.43 (centralized: 0.52), Cohen's kappa of 0.42 (0.49), and weighted F1 of 0.69 (0.74). In the FFSL stage, client-specific models reached an average balanced accuracy of 0.77, Cohen's kappa of 0.62, and weighted F1 of 0.73 across four sites with heterogeneous event distributions. These results suggest that FFSL can support effective patient-adaptive seizure detection under realistic data-availability and privacy constraints.

Related papers

• 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)
• DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations [1.901880164206253]
  We present DiffEEG, a self-supervised foundation model for deep learning for EEG-based seizure detection.<n>Pre-trained on 1.3M unlabeled segments from the Temple University Hospital Seizure Corpus (TUHSZ), DiffEEG learns generic neural representations via a 1D U-Net with multi-head self-attention.<n>Under strict patient-wise evaluation, DiffEEG achieves 61% accuracy and 59% F1 for 4-class seizure subtyping, and 81% accuracy with 85% weighted F1 for binary detection.
  arXiv  Detail & Related papers  (2026-07-13T13:59:45Z)
• Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets [0.06372261626436676]
  The present work addresses the gap by validating the FedCVR framework on five publicly available real cardiovascular datasets.<n>Results demonstrate that FedCVR preserves its adaptive advantage on real data, achieving an F1-Score of 79.2% and AUC of 0.96.<n>The measured privacy cost on real data confirms the graceful degradation pattern observed in the synthetic experiments.
  arXiv  Detail & Related papers  (2026-07-07T18:51:13Z)
• Open-Set Vein Biometric Recognition with Deep Metric Learning [0.0]
  We rigorously evaluate the computational boundaries of Deep Metric Learning (DML) under strict open-set constraints.<n>Our approach learns discnative L2-normalised embeddings and employs prototype-based matching with a similarity threshold.<n>On the large-scale MMCBNU 6000 benchmark, our best model (ResNet50-CBAM) achieves an OSCR of 0.9945, AUROC of 0.9974, and EER of 1.57%, maintaining high identification accuracy (99.6% Rank-1) while robustly rejecting unknown subjects.
  arXiv  Detail & Related papers  (2026-04-16T11:03:11Z)
• 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)
• MedHE: Communication-Efficient Privacy-Preserving Federated Learning with Adaptive Gradient Sparsification for Healthcare [0.0]
  This paper presents MedHE, a novel framework combining adaptive gradient sparsification with CKKS homomorphic encryption to enable privacy-preserving collaborative learning on sensitive medical data.<n>Our approach introduces a dynamic threshold mechanism with error compensation for top-k gradient selection, achieving 97.5 percent communication reduction while preserving model utility.
  arXiv  Detail & Related papers  (2025-11-12T06:50:48Z)
• Federated Learning for Epileptic Seizure Prediction Across Heterogeneous EEG Datasets [2.5069344340760717]
  This paper investigates FL for seizure prediction using a single EEG channel across four diverse public datasets (Siena, CHB-MIT, Helsinki, NCH)<n>We implement privacy-preserving global normalization and propose a Random Subset Aggregation strategy, where each client trains on a fixed-size random subset of its data per round, ensuring equal contribution during aggregation.<n>Our results show that locally trained models fail to generalize across sites, and standard weighted FedAvg yields highly skewed performance (e.g., 89.0% accuracy on CHB-MIT but only 50.8% on Helsinki and 50.6% on NCH)
  arXiv  Detail & Related papers  (2025-08-11T16:36:31Z)
• PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets [0.0]
  We introduce an innovative, open-source machine-learning framework that enables robust seizure detection across varied clinical datasets.<n>To enhance robustness, the framework incorporates an automated pre-processing pipeline to standardise data and a majority voting mechanism.<n>We train, tune, and evaluate models within each dataset, assessing their cross-dataset transferability.
  arXiv  Detail & Related papers  (2025-08-10T09:12:29Z)
• 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)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• Exploring traditional machine learning for identification of pathological auscultations [0.39577682622066246]
  Digital 6-channel auscultations of 45 patients were used in various machine learning scenarios. The aim was to distinguish between normal and anomalous pulmonary sounds. Supervised models showed a consistent advantage over unsupervised ones.
  arXiv  Detail & Related papers  (2022-09-01T18:03:21Z)
• Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis [48.64462717254158]
  We developed a self-supervised contrastive learning approach, EchoCLR, to catered to echocardiogram videos. When fine-tuned on small portions of labeled data, EchoCLR pretraining significantly improved classification performance for left ventricular hypertrophy (LVH) and aortic stenosis (AS) EchoCLR is unique in its ability to learn representations of medical videos and demonstrates that SSL can enable label-efficient disease classification from small, labeled datasets.
  arXiv  Detail & Related papers  (2022-07-23T19:17:26Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability [54.86482395312936]
  Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
  arXiv  Detail & Related papers  (2021-02-18T21:14:52Z)

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.