标题： FedCLAM：用于联邦医学图像分割的客户端自适应动量与前景强度匹配 显示英文标题

标题： FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentation

摘要： 联邦学习是一种去中心化的训练方法，在保持数据由利益相关者控制的同时，能够在孤立训练上实现更优的性能。 尽管机构间特征差异在所有联邦设置中都是一个挑战，但由于不同的成像设备和人口差异，医学影像尤其受到影响，这可能会降低全局模型的有效性。 现有的聚合方法通常无法适应各种情况。 为了解决这个问题，我们提出了FedCLAM，它结合了每个客户端在本地训练期间损失减少产生的\textit{客户端自适应动量}项，以及一个\textit{个性化阻尼因子}来防止过拟合。 我们进一步引入了一种新颖的\textit{强度对齐}损失，用于匹配预测和真实前景分布，以处理机构和设备之间的异构图像强度配置。 在两个数据集上的广泛评估表明，FedCLAM在医学分割任务中优于八种最先进的方法，证明了其有效性。 代码可在 https://github.com/siomvas/FedCLAM 获取。 显示英文摘要

摘要： Federated learning is a decentralized training approach that keeps data under stakeholder control while achieving superior performance over isolated training. While inter-institutional feature discrepancies pose a challenge in all federated settings, medical imaging is particularly affected due to diverse imaging devices and population variances, which can diminish the global model's effectiveness. Existing aggregation methods generally fail to adapt across varied circumstances. To address this, we propose FedCLAM, which integrates \textit{client-adaptive momentum} terms derived from each client's loss reduction during local training, as well as a \textit{personalized dampening factor} to curb overfitting. We further introduce a novel \textit{intensity alignment} loss that matches predicted and ground-truth foreground distributions to handle heterogeneous image intensity profiles across institutions and devices. Extensive evaluations on two datasets show that FedCLAM surpasses eight cutting-edge methods in medical segmentation tasks, underscoring its efficacy. The code is available at https://github.com/siomvas/FedCLAM.