Bayesian Similarity-Weighted Aggregation for Federated Brain Tumor Segmentation

Abstract

We propose a Bayesian generative approach, Bayesian Similarity-weighted Aggregation (SimAgg), for combining model weights from federated collaborators in brain lesion segmentation. This method effectively adapts to data variability and incorporates probabilistic modeling to handle uncertainty, enhancing robustness in federated learning (FL). Using a novel multi-armed bandit setup, it dynamically selects collaborators to improve aggregation quality. Simulation results on multi-parametric MRI data show that Bayesian SimAgg achieves high Dice scores across tumor regions and converges approximately twice as fast as non-Bayesian methods, providing an effective framework for federated brain tumor segmentation.

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Cite

Text

Khan et al. "Bayesian Similarity-Weighted Aggregation for Federated Brain Tumor Segmentation." NeurIPS 2024 Workshops: MusIML, 2024.

Markdown

[Khan et al. "Bayesian Similarity-Weighted Aggregation for Federated Brain Tumor Segmentation." NeurIPS 2024 Workshops: MusIML, 2024.](https://mlanthology.org/neuripsw/2024/khan2024neuripsw-bayesian/)

BibTeX

@inproceedings{khan2024neuripsw-bayesian,
  title     = {{Bayesian Similarity-Weighted Aggregation for Federated Brain Tumor Segmentation}},
  author    = {Khan, Muhammad Irfan and Khan, Suleiman A. and Kontio, Elina and Jafaritadi, Mojtaba},
  booktitle = {NeurIPS 2024 Workshops: MusIML},
  year      = {2024},
  url       = {https://mlanthology.org/neuripsw/2024/khan2024neuripsw-bayesian/}
}