Decentralized Gossip Mutual Learning (GML) for automatic head and neck
  tumor segmentation
Jingyun Chen Yading Yuan
Federated learning (FL) has emerged as a promising strategy for
collaboratively training complicated machine learning models from different
medical centers without the need of data sharing. However, the traditional FL
relies on a central server to orchestrate the global model training among
clients. This makes it vulnerable to the failure of the model server.
Meanwhile, the model trained based on the global data property may not yield
the best performance on the local data of a particular site due to the
variations of data characteristics among them. To address these limitations, we
proposed Gossip Mutual Learning(GML), a decentralized collaborative learning
framework that employs Gossip Protocol for direct peer-to-peer communication
and encourages each site to optimize its local model by leveraging useful
information from peers through mutual learning. On the task of tumor
segmentation on PET/CT images using HECKTOR21 dataset with 223 cases from five
clinical sites, we demonstrated GML could improve tumor segmentation
performance in terms of Dice Similarity Coefficient (DSC) by 3.2%, 4.6% and
10.4% on site-specific testing cases as compared to three baseline methods:
pooled training, FedAvg and individual training, respectively. We also showed
GML has comparable generalization performance as pooled training and FedAvg
when applying them on 78 cases from two out-of-sample sites where no case was
used for model training. In our experimental setup, GML showcased a sixfold
decrease in communication overhead compared to FedAvg, requiring only 16.67% of
the total communication overhead.