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

arXiv: https://arxiv.org/abs/2401.15434 · HTML: https://mlsanity.com/paper/2401.15434/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Automatic Brain Tumor Segmentation with Scale Attention Network](https://mlsanity.com/paper/2011.03188/) (2020)

## Abstract (arXiv)

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.