Summary of Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation, by Muhammad Irfan Khan et al.
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation
by Muhammad Irfan Khan, Elina Kontio, Suleiman A. Khan, Mojtaba Jafaritadi
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed client selection protocol optimizes Federated Learning (FL) for the Federated Tumor Segmentation Challenge (FeTS 2024). By leveraging non-negative matrix factorization (NNMF) and a hybrid aggregation approach, this method intelligently selects collaborators based on historical performance, expertise, and other relevant metrics. This addresses the cold start problem and improves FL process efficiency and precision. The protocol is evaluated using a dataset of mpMRI scans from glioblastoma patients, achieving dice scores for enhancing tumor, tumor core, and whole tumor segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps improve Federated Learning by choosing the right collaborators. It’s like finding the best team members to work on a project together. The researchers used a special way of combining data and expert information to pick the best collaborators. This made their learning process faster and more accurate. They tested it with brain tumor segmentation and got good results. |
Keywords
» Artificial intelligence » Federated learning » Precision
