Lightweight federated learning for STIs/HIV prediction

• Published in: Scientific reports Vol. 14; no. 1; pp. 6560 - 12
• Main Authors: Nguyen, Thi Phuoc Van, Yang, Wencheng, Tang, Zhaohui, Xia, Xiaoyu, Mullens, Amy B., Dean, Judith A., Li, Yan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.03.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/61; 692/700; Communication; Communications systems; Health risks; HIV; HIV Infections - epidemiology; Homosexuality, Male; Human immunodeficiency virus; Humanities and Social Sciences; Humans; Learning; Male; multidisciplinary; Risk assessment; Science; Science (multidisciplinary); Sexual and Gender Minorities; Sexually transmitted diseases; Sexually Transmitted Diseases - epidemiology; STD

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...