Data heterogeneity between clients remains a key challenge in Federated Learning (FL), particularly in the case of tabular data. This work presents Global Layers (GL), a novel partial model personalization method robust in the presence of joint distribution $P(X,Y)$ shift and mixed input/output spaces $X \times Y$ across clients. To the best of our knowledge, GL is the first method capable of supporting both client-exclusive features and classes. We introduce two new benchmark experiments for tabular FL naturally partitioned from existing real world datasets: i) UCI Covertype split into 4 clients by "wilderness area" feature, and ii) UCI Heart Disease, SAHeart, UCI Heart Failure, each as clients. Empirical results in these experiments in the full-participant setting show that GL achieves better outcomes than Federated Averaging (FedAvg) and local-only training, with some clients even performing better than their centralized baseline.

该研究提出了全局层（GL），一种新颖的部分模型个性化方法，能够在客户端之间出现G（X，Y）分布变化时保持鲁棒性，并且支持客户端专属的特征和类别。与Federated Averaging和本地训练相比，针对表格数据中的联邦学习， GL在两个新的基准实验中的效果优于它们，并且甚至有些客户端的表现比其集中式基线更好。

全局层：非独立同分布表格联邦学习