Patient-centric federated learning: automating meaningful consent to health data sharing with smart contracts

Abstract

Federated Learning (FL) promises to enhance data-driven health research by enabling collaborative machine learning across distributed datasets without direct data exchange. However, current FL implementations primarily reflect the data-sharing interests of institutional controllers rather than those of individual patients whose data are at stake. Existing consent mechanisms-like broad consent under HIPAA or explicit consent under the GDPR-fail to provide patients with control over how their data is used. This article explores the integration of smart contracts (SCs) into FL as a mechanism for automating, enforcing, and documenting consent in data transactions. SCs, encoded in decentralized ledger technologies, can ensure that FL processes align with patient preferences by providing an immutable, and dynamically updatable consent architecture. Integrating SCs into FL and swarm learning (SL) frameworks can mitigate ethico-legal concerns related to patient autonomy, data re-identification, and data use. This approach addresses persistent principle-agent asymmetries in biomedical data sharing by ensuring that patients, rather than data controllers alone, can specify the terms of access to insights derived from their health data. We discuss the implications of this model for regulatory compliance, data governance, and patient engagement, emphasizing its potential to foster public trust in health data ecosystems.