Lightweight federated learning for STIs/HIV prediction

Abstract

This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy ($$93\%$$ 93 % ). Despite these promising findings, a limitation of the study lies in the experiment for man’s data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework’s impact and ultimately aim to improve health outcomes/health service optimisation.

Ethical Considerations for Artificial Intelligence Applications for HIV

“…The distributed nature of the AI system may come at the cost of decreased overall performance, hinder the development of a robust global model, and expose the system to be more vulnerable to malicious attacks [15]. A study that applied FL in an AI system for HIV was conducted by Nguyen et al, who proposed a FL framework for predicting the risk of sexually transmissible infections (STIs) and HIV [16]. The FL system encompassed multiple clinics and key stakeholders.…”