[2604.02248] BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy

Statistics > Machine Learning arXiv:2604.02248 (stat) [Submitted on 2 Apr 2026] Title:BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy Authors:Abhilash Kar, Basisth Saha, Tanmay Sen, Biswabrata Pradhan View a PDF of the paper titled BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy, by Abhilash Kar and 3 other authors View PDF HTML (experimental) Abstract:Multimodal time-to-event prediction often requires integrating sensitive data distributed across multiple parties, making centralized model training impractical due to privacy constraints. At the same time, most existing multimodal survival models produce single deterministic predictions without indicating how confident the model is in its estimates, which can limit their reliability in real-world decision making. To address these challenges, we propose BVFLMSP, a Bayesian Vertical Federated Learning (VFL) framework for multimodal time-to-event analysis based on a Split Neural Network architecture. In BVFLMSP, each client independently models a specific data modality using a Bayesian neural network, while a central server aggregates intermediate representations to perform survival risk prediction. To enhance privacy, we integrate differential privacy mechanisms by perturbing client side representations before transmission, providing formal privacy guarantees against information leakage during federated training. We first evaluate our Bayesian multimodal ...