mlsanityData to 14 Oct 2026

Federated brain tumor segmentation: an extensive benchmark

arXiv 2410.17265 ↗First posted 7 Oct 2024cs.CV cs.AI cs.LGcited by 5

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Abstract (arXiv)

Recently, federated learning has raised increasing interest in the medical image analysis field due to its ability to aggregate multi-center data with privacy-preserving properties. A large amount of federated training schemes have been published, which we categorize into global (one final model), personalized (one model per institution) or hybrid (one model per cluster of institutions) methods. However, their applicability on the recently published Federated Brain Tumor Segmentation 2022 dataset has not been explored yet. We propose an extensive benchmark of federated learning algorithms from all three classes on this task. While standard FedAvg already performs very well, we show that some methods from each category can bring a slight performance improvement and potentially limit the final model(s) bias toward the predominant data distribution of the federation. Moreover, we provide a deeper understanding of the behaviour of federated learning on this task through alternative ways of distributing the pooled dataset among institutions, namely an Independent and Identical Distributed (IID) setup, and a limited data setup.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 66 of its 85 references that are in the map. First posted on arXiv 7 Oct 2024.

Builds on

66 of 85 references.

References · 66

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38 more
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50 most cited of 66 All 66 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025A survey on Clustered Federated Learning: Taxonomy, Analysis and Applicationscited by 2
  2. 2025Exploring Federated Learning for Thermal Urban Feature Segmentation -- A Comparison of Centralized and Decentralized Approachescited by 0
  3. 2026FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentationcited by 0
  4. 2026Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapycited by 0
  5. 2026MedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shiftcited by 0