Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI

Jingyun Chen, Yading Yuan
1/27/2024
eess.IVcs.CVcs.LG

No confident code implementation found for this paper.

Abstract

Federated Learning (FL) enables collaborative model training among medical centers without sharing private data. However, traditional FL risks on server failures and suboptimal performance on local data due to the nature of centralized model aggregation. To address these issues, we present Gossip Mutual Learning (GML), a decentralized framework that uses Gossip Protocol for direct peer-to-peer communication. In addition, GML encourages each site to optimize its local model through mutual learning to account for data variations among different sites. For the task of tumor segmentation using 146 cases from four clinical sites in BraTS 2021 dataset, we demonstrated GML outperformed local models and achieved similar performance as FedAvg with only 25% communication overhead.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation

96% match2024

Federated brain tumor segmentation: an extensive benchmark

85% match2024

Medical Federated Model with Mixture of Personalized and Sharing Components

84% match1 repo62023

FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation

83% match2025

Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities

83% match1 repo62024

Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities

82% match1 repo152024

Cite this paper

@article{chen2024decentralized,
  title  = {Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI},
  author = {Jingyun Chen and Yading Yuan},
  year   = {2024},
  eprint = {2401.15434},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.15434v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.15434.svg)](https://codeofpaper.com/paper/2401.15434)

Discussion