mlsanityData to 14 Oct 2026

Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations

arXiv 2304.14976 ↗First posted 28 Apr 2023cs.CV cs.LG eess.IVcited by 4

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Abstract (arXiv)

SplitFed Learning, a combination of Federated and Split Learning (FL and SL), is one of the most recent developments in the decentralized machine learning domain. In SplitFed learning, a model is trained by clients and a server collaboratively. For image segmentation, labels are created at each client independently and, therefore, are subject to clients' bias, inaccuracies, and inconsistencies. In this paper, we propose a data quality-based adaptive averaging strategy for SplitFed learning, called QA-SplitFed, to cope with the variation of annotated ground truth (GT) quality over multiple clients. The proposed method is compared against five state-of-the-art model averaging methods on the task of learning human embryo image segmentation. Our experiments show that all five baseline methods fail to maintain accuracy as the number of corrupted clients increases. QA-SplitFed, however, copes effectively with corruption as long as there is at least one uncorrupted client.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 13 of its 19 references that are in the map. First posted on arXiv 28 Apr 2023.

Builds on

13 of 19 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  4. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  5. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  6. 2018Distributed learning of deep neural network over multiple agentscited by 279
  7. 2019Split Learning for collaborative deep learning in healthcarecited by 73
  8. 2020Robust Federated Learning with Noisy Labelscited by 40
  9. 2021Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learningcited by 23
  10. 2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 23
  11. 2021SplitAVG: A heterogeneity-aware federated deep learning method for medical imagingcited by 19
  12. 2020Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcarecited by 14
1 more
  1. 2021Auto-weighted Robust Federated Learning with Corrupted Data Sourcescited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2023SplitFed resilience to packet loss: Where to split, that is the questioncited by 2
  2. 2024Optimizing Split Points for Error-Resilient SplitFed Learningcited by 1
  3. 2025MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentationcited by 0
  4. 2026SplitFed-CL: A Split Federated Co-Learning Framework for Medical Image Segmentation with Inaccurate Labelscited by 0