Vertical federated learning based on data subset representation for healthcare application.
Journal:
Computer methods and programs in biomedicine
PMID:
39954511
Abstract
BACKGROUND AND OBJECTIVE: Artificial intelligence is increasingly essential for disease classification and clinical diagnosis tasks in healthcare. Given the strict privacy needs of healthcare data, Vertical Federated Learning (VFL) has been introduced. VFL allows multiple hospitals to collaboratively train models on vertically partitioned data, where each holds only the patient's partial data features, thus maintaining patient confidentiality. However, VFL applications in healthcare scenarios with fewer samples and labels are challenging because existing methods heavily depend on labeled samples and do not consider the intrinsic connections among the data across hospitals.