mlsanityData to 14 Oct 2026

Privacy-Aware Federated nnU-Net for ECG Page Digitization

arXiv 2510.22387 ↗First posted 25 Oct 2025cs.CR cs.CV cs.LGcited by 0

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Abstract (arXiv)

Deep neural networks can convert ECG page images into analyzable waveforms, yet centralized training often conflicts with cross-institutional privacy and deployment constraints. A cross-silo federated digitization framework is presented that trains a full-model nnU-Net segmentation backbone without sharing images and aggregates updates across sites under realistic non-IID heterogeneity (layout, grid style, scanner profile, noise). The protocol integrates three standard server-side aggregators--FedAvg, FedProx, and FedAdam--and couples secure aggregation with central, user-level differential privacy to align utility with formal guarantees. Key features include: (i) end-to-end full-model training and synchronization across clients; (ii) secure aggregation so the server only observes a clipped, weighted sum once a participation threshold is met; (iii) central Gaussian DP with Renyi accounting applied post-aggregation for auditable user-level privacy; and (iv) a calibration-aware digitization pipeline comprising page normalization, trace segmentation, grid-leakage suppression, and vectorization to twelve-lead signals. Experiments on ECG pages rendered from PTB-XL show consistently faster convergence and higher late-round plateaus with adaptive server updates (FedAdam) relative to FedAvg and FedProx, while approaching centralized performance. The privacy mechanism maintains competitive accuracy while preventing exposure of raw images or per-client updates, yielding deployable, auditable guarantees suitable for multi-institution settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 36 references that are in the map. First posted on arXiv 25 Oct 2025.

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15 of 36 references.

References · 15

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2020Contrastive Learning of Medical Visual Representations from Paired Images and Textcited by 641
  7. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  8. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  9. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  10. 2022Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentationcited by 42
  11. 2022Personalizing Federated Medical Image Segmentation via Local Calibrationcited by 29
  12. 2023ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitizationcited by 20
3 more
  1. 2023Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentationcited by 4
  2. 2025Comparative Analysis of Data Augmentation for Clinical ECG Classification with STARcited by 3
  3. 2025Enhancing Maritime Object Detection in Real-Time with RT-DETR and Data Augmentationcited by 2

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