Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation
  on multi-parametric MRI
Jingyun Chen Yading Yuan
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.