Advancing Disability Healthcare Solutions Through Privacy-Preserving Federated Learning With Theme Framework

Abstract

The application of machine learning, particularly federated learning, in collaborative model training, has demonstrated significant potential for enhancing diversity and efficiency in outcomes. In the healthcare domain, particularly healthcare with disabilities, the sensitive nature of data presents a significant challenge as sharing even the computation on these data can risk exposing personal health information. This research addresses the problem of enabling shared model training for healthcare data—particularly with disabilities decreasing the risk of leaking or compromising sensitive information. Technologies such as federated learning provide solution for decentralised model training but fall short in addressing concerns related to trust building, accountability and control over participation and data. We propose a framework that integrates federated learning with advanced identity management as well as privacy and trust management technologies. Our framework called Theme (Trusted Healthcare Machine Learning Environment) leverages digital identities (e.g., W3C decentralised identifiers and verified credentials) and policy enforcements to regulate participation. This is to ensure that only authorised and trusted entities can contribute to the model training. Additionally, we introduce the mechanisms to track contributions per participant and offer the flexibility for participants to opt out of model training at any point. Participants can choose to be either contributors (providers) or consumers (model users) or both, and they can also choose to participate in subset of activities. This is particularly important in healthcare settings, where individuals and healthcare institutions have the flexibility to control how their data are used without compromising the benefits. In summary, this research work contributes to privacy preserving shared model training leveraging federated learning without exposing sensitive data; trust and accountability mechanisms; contribution tracking per participant for accountability and back-tracking; and fine-grained control and autonomy per participant. By addressing the specific needs of healthcare data for people with disabilities or such institutions, the Theme framework offers a robust solution to balance the benefits of shared machine learning with critical need to protecting sensitive data.