  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.
