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.

翻译：联邦学习（FL）已成为一种有前景的策略，可在无需数据共享的情况下，从不同医疗中心协作训练复杂机器学习模型。然而，传统FL依赖中央服务器协调客户端间的全局模型训练，这使得其易受模型服务器故障影响。同时，基于全局数据特性训练的模型可能无法在特定地点的本地数据上实现最优性能，因为不同地点的数据特征存在差异。为解决这些局限性，我们提出了八卦互学习（GML）——一种去中心化协作学习框架，采用八卦协议实现点对点直接通信，并通过互学习鼓励每个站点利用来自同伴的有用信息优化其本地模型。在基于HECKTOR21数据集（包含来自五个临床中心的223例病例）的PET/CT图像肿瘤分割任务中，我们证明了GML在站点特定测试病例上相比三种基线方法（合并训练、FedAvg和独立训练），Dice相似系数（DSC）分别提升了3.2%、4.6%和10.4%。我们还展示了GML在78例来自两个未参与模型训练的外部样本集病例上的泛化性能与合并训练及FedAvg相当。在我们的实验设置中，GML相比FedAvg减少了六倍的通信开销，仅需其总通信开销的16.67%。