MediVault: An Auditable and Secure Federated Learning System for Privacy-Preserving Healthcare Collaboration

Healthcare analytics is often limited by amounts of data and strict privacy requirements, which make it difficult to share patient-level records across organisations and to build robust predictive models. Federated learning (FL) provides an alternative by keeping data local and exchanging model updates instead of raw records. However, many existing FL solutions remain difficult to deploy in healthcare settings, as they provide limited support for auditability, governance-oriented evidence, and system-level transparency. This paper presents MediVault, an auditable and secure federated learning-based system for privacy-preserving healthcare collaboration. MediVault combines round-based federated training, protected update exchange, audit-ready telemetry, and an interactive dashboard that exposes non-sensitive evidence of collaboration, model progress, and protocol execution. In addition, the system supports controlled reporting to improve stakeholder communication during pilot deployments. We evaluate MediVault on two public healthcare classification datasets, Breast Cancer Wisconsin (Diagnostic) and Heart Disease, under settings designed to reflect multi-site heterogeneity. Experiments are conducted using two interpretable linear models, logistic regression and linear SVM, under matched settings. Results show that federated training remains competitive with centralised training across both datasets. These findings suggest that an auditable and secure FL workflow can preserve predictive utility while also supporting the transparency, governance readiness, and practical system behaviour needed for privacy-preserving multi-organisation healthcare collaboration.