A Unified Framework for Vertical Federated Learning with Partially Missing Features and Locally Independent Inference

Vertical Federated Learning enables collaborative training across clients holding disjoint feature subsets of data samples. Existing methods often treat a client's local features as either fully available or entirely missing, and thus fail to make full use of partially observed features. Moreover, standard VFL typically relies on joint inference involving all participating clients, limiting the ability of an individual client to make predictions independently. To address these limitations, we propose XVFL, a unified framework that uses non-aligned data with partially missing features and supports locally independent inference for each client. Specifically, XVFL introduces two modules: Cross Completion (XCom), which reconstructs missing local features for non-aligned samples, and Decision Subspace Alignment (DSAlign), which aligns local, completed, and joint representations in the decision subspace to improve independent inference. Furthermore, we establish convergence guarantees for XVFL, proving an $O(1/\sqrt{T})$ rate for SGD-type and $O(1/T)$ for PAGE-type algorithms, where $T$ denotes the number of training steps. Extensive experiments on real-world datasets demonstrate that XVFL significantly outperforms existing methods across different feature missing rates, e.g., achieving an improvement of around 43 percentage points on the medical MIMIC-III dataset, validating XVFL's practical effectiveness and robustness.