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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 (\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$93\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>93<\/mml:mn>\n                            <mml:mo>%<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ). Despite these promising findings, a limitation of the study lies in the experiment for man\u2019s 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\u2019s impact and ultimately aim to improve health outcomes\/health service optimisation.\n                  <\/jats:p>","DOI":"10.1038\/s41598-024-56115-0","type":"journal-article","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T07:02:05Z","timestamp":1710831725000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Lightweight federated learning for STIs\/HIV prediction"],"prefix":"10.1038","volume":"14","author":[{"given":"Thi Phuoc Van","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wencheng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohui","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy B.","family":"Mullens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judith A.","family":"Dean","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,19]]},"reference":[{"key":"56115_CR1","first-page":"441","volume":"45","author":"S Xu","year":"2007","unstructured":"Xu, S., Huang, X., Xu, H. & Zhang, C. 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