Vertical Federated Learning with Missing Features During Training and Inference
Machine Learning in Montpellier, Theory & Practice - Pedro Valdeira
Vertical federated learning trains models from feature-partitioned datasets across multiple clients, who collaborate without sharing their local data. Standard approaches assume that all feature partitions are available during both training and inference. Yet, in practice, this assumption rarely holds, as for many samples only a subset of the clients observe their partition. However, not utilizing incomplete samples during training harms generalization, and not supporting them during inference limits the utility of the model.
Vertical Federated Learning, Missing Feature Blocks, LabéliséHallesIA