{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:13:32Z","timestamp":1782846812671,"version":"3.54.5"},"reference-count":87,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T00:00:00Z","timestamp":1652400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology of the Republic of China","award":["MOST109-2221-E-001-019-MY3"],"award-info":[{"award-number":["MOST109-2221-E-001-019-MY3"]}]},{"name":"Ministry of Science and Technology of the Republic of China","award":["AS-KPQ-109-DSTCP"],"award-info":[{"award-number":["AS-KPQ-109-DSTCP"]}]},{"DOI":"10.13039\/501100001869","name":"Academia Sinica","doi-asserted-by":"publisher","award":["MOST109-2221-E-001-019-MY3"],"award-info":[{"award-number":["MOST109-2221-E-001-019-MY3"]}],"id":[{"id":"10.13039\/501100001869","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001869","name":"Academia Sinica","doi-asserted-by":"publisher","award":["AS-KPQ-109-DSTCP"],"award-info":[{"award-number":["AS-KPQ-109-DSTCP"]}],"id":[{"id":"10.13039\/501100001869","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model\u2019s accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.<\/jats:p>","DOI":"10.3390\/s22103728","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"3728","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information"],"prefix":"10.3390","volume":"22","author":[{"given":"Trang-Thi","family":"Ho","sequence":"first","affiliation":[{"name":"Research Center for Information Technology Innovation, Academia Sinica, Taipei 10607, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khoa-Dang","family":"Tran","sequence":"additional","affiliation":[{"name":"Research Center for Information Technology Innovation, Academia Sinica, Taipei 10607, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9312-0113","authenticated-orcid":false,"given":"Yennun","family":"Huang","sequence":"additional","affiliation":[{"name":"Research Center for Information Technology Innovation, Academia Sinica, Taipei 10607, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"ref_1","unstructured":"CNN\u2019s John Bonifield (2021, June 12). 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