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Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare

arXiv 2012.12591 ↗First posted 23 Dec 2020cs.LG cs.CRcited by 14

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Abstract (arXiv)

In this paper, we compare three privacy-preserving distributed learning techniques: federated learning, split learning, and SplitFed. We use these techniques to develop binary classification models for detecting tuberculosis from chest X-rays and compare them in terms of classification performance, communication and computational costs, and training time. We propose a novel distributed learning architecture called SplitFedv3, which performs better than split learning and SplitFedv2 in our experiments. We also propose alternate mini-batch training, a new training technique for split learning, that performs better than alternate client training, where clients take turns to train a model.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Dec 2020.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 1; 2024: 4; 2025: 1; 2026: 1.

14 papers.

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  1. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  2. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  3. 2021AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learningcited by 8
  4. 2021Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practicecited by 6
  5. 2024Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networkscited by 6
  6. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  7. 2023Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotationscited by 4
  8. 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 3
  9. 2024Parallel Split Learning with Global Samplingcited by 3
  10. 2021Vulnerability Due to Training Order in Split Learningcited by 2
  11. 2025Data Heterogeneity and Forgotten Labels in Split Federated Learningcited by 2
  12. 2022Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Modelcited by 1
2 more
  1. 2021Levels of Autonomous Radiologycited by 0
  2. 2026Smart Split-Federated Learning over Noisy Channels for Embryo Image Segmentationcited by 0