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arXiv · 2305.19290

Global Layers: Non-IID Tabular Federated Learning

Yazan Obeidi
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Abstract

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.

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Yazan Obeidi. 2023-05-29. Global Layers: Non-IID Tabular Federated Learning. https://arxiv.org/abs/2305.19290

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