{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:28:16Z","timestamp":1766068096673,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"vor","delay-in-days":24,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"U.S. National Science Foundation","award":["2434487"],"award-info":[{"award-number":["2434487"]}]},{"name":"U.S. National Science Foundation","award":["2200138"],"award-info":[{"award-number":["2200138"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"DOI":"10.1145\/3698587.3701396","type":"proceedings-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T10:05:08Z","timestamp":1734343508000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Efficient Federated Learning with Multi-Teacher Knowledge Distillation for COVID-19 Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3254-6453","authenticated-orcid":false,"given":"Richard","family":"Annan","sequence":"first","affiliation":[{"name":"Department of Computer Science, North Carolina A&T State University, Greensboro, North Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1060-6722","authenticated-orcid":false,"given":"Hong","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Old Dominion University, Norfolk, Virginia, U.S.A"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1295-9812","authenticated-orcid":false,"given":"Xiaohong","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Carolina A&T State University, Greensboro, North Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9026-5322","authenticated-orcid":false,"given":"Kaushik","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Carolina A&T State University, Greensboro, North Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3865-0266","authenticated-orcid":false,"given":"Robert","family":"Newman","sequence":"additional","affiliation":[{"name":"Department of Biology, North Carolina A&T State University, Greensboro, North Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9745-9584","authenticated-orcid":false,"given":"Letu","family":"Qingge","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Carolina A&T State University, Greensboro, North Carolina, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"volume-title":"Proceedings of the 16th International Conference on Bioinformatics and Computational Biology (BICOB). New Orleans, 1--12","author":"Annan R.","key":"e_1_3_2_1_1_1","unstructured":"R. Annan, H. Qin, and L. Qingge. 2024. Generalizing Deep Learning for COVID-19 Detection with Transfer and Continuous Learning. In Proceedings of the 16th International Conference on Bioinformatics and Computational Biology (BICOB). New Orleans, 1--12."},{"key":"e_1_3_2_1_2_1","volume-title":"COVID-19 Image Data Collection: Prospective Predictions Are the Future. arXiv","author":"Cohen Joseph Paul","year":"2006","unstructured":"Joseph Paul Cohen, Paul Morrison, Lan Dao, Karsten Roth, Tim Q Duong, and Marzyeh Ghassemi. 2020. COVID-19 Image Data Collection: Prospective Predictions Are the Future. arXiv 2006.11988 (2020). https:\/\/github.com\/ieee8023\/covid-chestxray-dataset"},{"key":"e_1_3_2_1_3_1","volume-title":"Generalizable segmentation of COVID-19 infection from multi-site tomography scans: a federated learning framework","author":"Ding Weiping","year":"2023","unstructured":"Weiping Ding, Mohamed Abdel-Basset, Hossam Hawash, Mahardhika Pratama, and Witold Pedrycz. 2023. Generalizable segmentation of COVID-19 infection from multi-site tomography scans: a federated learning framework. IEEE Transactions on Emerging Topics in Computational Intelligence (2023)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.892499"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.47723\/kcmj.v18i3.828"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107330"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"M. Ghaderzadeh F. Asadi R. Jafari D. Bashash H. Abolghasemi and M. Aria. 2021 4. Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study. J Med Internet Res 23 (2021 4) e27468. http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/33848973.","DOI":"10.2196\/27468"},{"key":"e_1_3_2_1_8_1","volume-title":"Isabelle Bloch, and Pietro Gori.","author":"Hu Minhao","year":"2020","unstructured":"Minhao Hu, Matthis Maillard, Ya Zhang, Tommaso Ciceri, Giammarco La Barbera, Isabelle Bloch, and Pietro Gori. 2020. Knowledge Distillation from Multi-modal to Mono-modal Segmentation Networks. Springer International Publishing, 772--781."},{"key":"e_1_3_2_1_9_1","volume-title":"Comparing kullback-leibler divergence and mean squared error loss in knowledge distillation. arXiv preprint arXiv:2105.08919","author":"Kim Taehyeon","year":"2021","unstructured":"Taehyeon Kim, Jaehoon Oh, NakYil Kim, Sangwook Cho, and Se-Young Yun. 2021. Comparing kullback-leibler divergence and mean squared error loss in knowledge distillation. arXiv preprint arXiv:2105.08919 (2021)."},{"key":"e_1_3_2_1_10_1","volume-title":"On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189","author":"Li Xiang","year":"2019","unstructured":"Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2019. On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189 (2019)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"M. Maftouni A. Law B. Shen Y. Zhou N. Ayoobi Yazdi and Z. Kong. 2021 6. A Robust Ensemble-Deep Learning Model for COVID-19 Diagnosis based on an Integrated CT Scan Images Database.","DOI":"10.36227\/techrxiv.18166667"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.2110.07136"},{"key":"e_1_3_2_1_13_1","volume-title":"Knowledge Distillation for Mobile Quantitative Ultrasound Imaging. In 2022 IEEE International Ultrasonics Symposium (IUS). IEEE.","author":"Oh Seok-Hwan","year":"2022","unstructured":"Seok-Hwan Oh, Myeong-Gee Kim, Young-Min Kim, Guil Jung, Hyuk-Sool Kwon, and Hyeon-Min Bae. 2022. Knowledge Distillation for Mobile Quantitative Ultrasound Imaging. In 2022 IEEE International Ultrasonics Symposium (IUS). IEEE."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.3008757"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3098703"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3031384"},{"key":"e_1_3_2_1_17_1","volume-title":"Adaptive federated optimization. arXiv preprint arXiv:2003.00295","author":"Reddi Sashank","year":"2020","unstructured":"Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Kone\u010dny, Sanjiv Kumar, and H Brendan McMahan. 2020. Adaptive federated optimization. arXiv preprint arXiv:2003.00295 (2020)."},{"key":"e_1_3_2_1_18_1","unstructured":"E. Soares P. Angelov S. Biaso M. Froes and D. Abe. 2020. SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. https:\/\/www.medrxiv.org\/content\/early\/2020\/05\/14\/2020.04.24.20078584."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.10.007"},{"key":"e_1_3_2_1_20_1","volume-title":"Towards Efficient Medical Image Segmentation Via Boundary-Guided Knowledge Distillation. In 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE.","author":"Wen Yang","year":"2021","unstructured":"Yang Wen, Leiting Chen, Shuo Xi, Yu Deng, Ximan Tang, and Chuan Zhou. 2021. Towards Efficient Medical Image Segmentation Via Boundary-Guided Knowledge Distillation. In 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10096427"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.2203.12803"}],"event":{"name":"BCB '24: 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"],"location":"Shenzhen China","acronym":"BCB '24"},"container-title":["Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698587.3701396","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3698587.3701396","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3698587.3701396","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:27:58Z","timestamp":1755862078000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698587.3701396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":22,"alternative-id":["10.1145\/3698587.3701396","10.1145\/3698587"],"URL":"https:\/\/doi.org\/10.1145\/3698587.3701396","relation":{},"subject":[],"published":{"date-parts":[[2024,11,22]]},"assertion":[{"value":"2024-12-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}