mlsanityData to 7 Oct 2026

Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI

arXiv 2302.11571 ↗First posted 20 Feb 2023eess.IV cs.CV cs.LGcited by 6

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Abstract (arXiv)

Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data. In this paper, we proposed PPPML-HMI, an open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis. To our best knowledge, personalization and privacy protection were achieved simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing our novel cyclic secure aggregation with the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans. For the real-world task, PPPML-HMI achieved ~5% higher Dice score on average compared to conventional FL under the heterogeneous scenario. Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the solid privacy-preserving capability of PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we further demonstrated the strong robustness of PPPML-HMI.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 44 of its 89 references that are in the map. First posted on arXiv 20 Feb 2023.

Builds on

44 of 89 references.

References · 44

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32 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 2.

6 papers.

  1. 2023SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Modelcited by 18
  2. 2023Path to Medical AGI: Unify Domain-specific Medical LLMs with the Lowest Costcited by 12
  3. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  4. 2025Federated Client-tailored Adapter for Medical Image Segmentationcited by 3
  5. 2026Communication-efficient Federated Graph Classification via Generative Diffusion Modelingcited by 0
  6. 2026Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Riskscited by 0