TY - JOUR

T1 - Patient-Centric Federated Learning: Automating Meaningful Consent to Health Data Sharing with Smart Contracts

AU - Kostick-Quenet, Kristin

AU - Corrales Compagnucci, Marcelo

AU - Minssen, Timo

AU - Aboy, Mateo

PY - 2024/4

Y1 - 2024/4

N2 - Federated learning (FL) promises to enhance data-driven learning models while protecting data for data controllers and processors, offering a critical tool to address global health challenges by accruing the benefits of “big data” without the need to duplicate, exchange or centralize them. While a promising solution, FL frameworks to date are more often implemented⁵ as mechanisms to observe data protection, sharing preferences and proprietary interests of institutions, organizations and corporations (data controllers) and do not inherently integrate mechanisms to observe data sharing preferences of individuals (patients; citizens) who are the primary subjects of those data (i.e., the identified or identifiable natural person that personal data relates to). Recently some of us⁹ presented a mechanism – smart contracts (SCs) – with potential to empower patients to actively participate in decisions about how and with whom data controllers share their personal health information (PHI). Here, we propose how SCs may be integrated into FL and other variants of decentralized learning like swarm learning (SL)¹⁴ to ensure meaningful (i.e., democratized, transparent, and efficient) patient consent to the exchange of health information. This approach contributes to efforts to create “trusted infrastructures” for data sharing supported by legislative initiatives in the US and Europe (Box 1).

AB - Federated learning (FL) promises to enhance data-driven learning models while protecting data for data controllers and processors, offering a critical tool to address global health challenges by accruing the benefits of “big data” without the need to duplicate, exchange or centralize them. While a promising solution, FL frameworks to date are more often implemented⁵ as mechanisms to observe data protection, sharing preferences and proprietary interests of institutions, organizations and corporations (data controllers) and do not inherently integrate mechanisms to observe data sharing preferences of individuals (patients; citizens) who are the primary subjects of those data (i.e., the identified or identifiable natural person that personal data relates to). Recently some of us⁹ presented a mechanism – smart contracts (SCs) – with potential to empower patients to actively participate in decisions about how and with whom data controllers share their personal health information (PHI). Here, we propose how SCs may be integrated into FL and other variants of decentralized learning like swarm learning (SL)¹⁴ to ensure meaningful (i.e., democratized, transparent, and efficient) patient consent to the exchange of health information. This approach contributes to efforts to create “trusted infrastructures” for data sharing supported by legislative initiatives in the US and Europe (Box 1).

M3 - Journal article

JO - Journal of Law and the Biosciences

JF - Journal of Law and the Biosciences

SN - 2053-9711

ER -