针对医疗影像联邦学习中的设备差异，提出自适应动量与强度对齐新方法。
FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentation
- 根据客户端训练损失动态调整动量，提升模型适应性。
- 在两个数据集上优于8种前沿方法，分割精度显著提升。
- 适合多机构医疗影像协作场景，尤其应对设备差异问题。
联邦学习是一种去中心化训练范式，可在保持数据隐私的同时实现优于孤立训练的性能。然而，跨机构特征差异在所有联邦学习场景中均构成挑战，而医疗影像因成像设备多样性和人群差异尤为严重，常导致全局模型效果下降。现有聚合方法难以适应复杂变化。为此，本文提出FedCLAM，融合客户端自适应动量项（基于本地训练期间的损失减少）和个性化抑制因子以缓解过拟合。此外，引入新颖的强度对齐损失，匹配预测与真实前景分布，有效处理不同机构间图像强度异质性。在两个数据集上的广泛实验表明，FedCLAM在医学图像分割任务中超越八种先进方法，验证了其有效性。代码已开源：https://github.com/siomvas/FedCLAM。
原文摘要 · Abstract (English)
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.
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