mlsanityData to 14 Oct 2026

Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis

arXiv 2407.02261 ↗First posted 2 Jul 2024cs.CVcited by 4

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Abstract (arXiv)

Medical image classification plays a crucial role in computer-aided clinical diagnosis. While deep learning techniques have significantly enhanced efficiency and reduced costs, the privacy-sensitive nature of medical imaging data complicates centralized storage and model training. Furthermore, low-resource healthcare organizations face challenges related to communication overhead and efficiency due to increasing data and model scales. This paper proposes a novel privacy-preserving medical image classification framework based on federated learning to address these issues, named FedMIC. The framework enables healthcare organizations to learn from both global and local knowledge, enhancing local representation of private data despite statistical heterogeneity. It provides customized models for organizations with diverse data distributions while minimizing communication overhead and improving efficiency without compromising performance. Our FedMIC enhances robustness and practical applicability under resource-constrained conditions. We demonstrate FedMIC's effectiveness using four public medical image datasets for classical medical image classification tasks.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 34 of its 52 references that are in the map. First posted on arXiv 2 Jul 2024.

Builds on

34 of 52 references.

References · 34

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22 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 2.

4 papers.

  1. 2024Federated Foundation Models on Heterogeneous Time Seriescited by 13
  2. 2024Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecturecited by 1
  3. 2026OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imagingcited by 1
  4. 2026FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocationcited by 0