通过模型功能距离优化联邦域适应，提升小样本目标场景的准确率。
FedDAF: Federated Domain Adaptation Using Model Functional Distance
- 基于梯度场夹角计算模型功能距离，动态加权聚合源模型。
- 在真实数据集上测试准确率优于现有联邦学习与域适应方法。
- 适合新加入联邦的临床等标签数据稀缺场景使用。
联邦域适应（FDA）通过协作源客户端在保护数据隐私的前提下提升目标客户端的模型性能。其面临两大挑战：源与目标数据间的域偏移，以及目标端标签数据有限——这在新站点加入联邦前缺乏自有标注数据时尤为常见，如临床部署场景。现有方法多仅关注域偏移问题，假设目标端有充足标签数据；即使处理数据稀缺的方法，也未根据目标具体任务对源信息进行优先排序。我们提出FedDAF，通过相似性聚合全局源与目标模型，利用其在目标数据上的均值梯度场夹角计算模型功能距离，并以Gompertz函数归一化。全局源模型通过距离加权平均构建，使更接近目标模型的源模型获得更高权重。在真实数据集上的实验表明，FedDAF在测试准确率上优于现有的联邦学习（FL）、个性化联邦学习及联邦域适应方法。
原文摘要 · Abstract (English)
Federated Domain Adaptation (FDA) improves model performance at a target client by collaborating with source clients while preserving data privacy. FDA faces two key challenges: domain shift between source and target data, and limited labeled data at the target, a common constraint when a new site joins a federation before it has accumulated its own labeled data, as in clinical deployments. Most existing methods address domain shift alone, assuming ample target data; those that also tackle data scarcity still fail to prioritize source information according to the target's specific objective. We propose FedDAF, which addresses both challenges through similarity-based aggregation of the global source and target models, using their model functional distance, computed from the angle between their mean gradient fields on target data and normalized via a Gompertz function. The global source model itself is formed using a distance-based weighted average, giving greater weight to source models closer to the target model. Experiments on real-world datasets show FedDAF outperforms existing federated learning (FL), personalized FL, and FDA methods in test accuracy.
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