{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:49:01Z","timestamp":1780764541670,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467185","type":"proceedings-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T18:21:39Z","timestamp":1628878899000},"page":"3845-3853","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":65,"title":["FLOP: Federated Learning on Medical Datasets using Partial Networks"],"prefix":"10.1145","author":[{"given":"Qian","family":"Yang","sequence":"first","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weituo","family":"Hao","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory P.","family":"Spell","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lawrence","family":"Carin","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2020. CORONAVIRUS. \"https:\/\/coronavirus.jhu.edu\/map.html\".  2020. CORONAVIRUS. \"https:\/\/coronavirus.jhu.edu\/map.html\"."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2014.2339736"},{"key":"e_1_3_2_1_3_1","first-page":"1333","article-title":"Privacy-preserving deep learning via additively homomorphic encryption","volume":"13","author":"Aono Yoshinori","year":"2017","unstructured":"Yoshinori Aono , Takuya Hayashi , Lihua Wang , Shiho Moriai , 2017 . Privacy-preserving deep learning via additively homomorphic encryption . IEEE Transactions on Information Forensics and Security , Vol. 13 , 5 (2017), 1333 -- 1345 . Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, et al. 2017. Privacy-preserving deep learning via additively homomorphic encryption. IEEE Transactions on Information Forensics and Security, Vol. 13, 5 (2017), 1333--1345.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"e_1_3_2_1_5_1","volume-title":"Ioannis Ch Paschalidis, and Wei Shi","author":"Brisimi Theodora S","year":"2018","unstructured":"Theodora S Brisimi , Ruidi Chen , Theofanie Mela , Alex Olshevsky , Ioannis Ch Paschalidis, and Wei Shi . 2018 . Federated learning of predictive models from federated electronic health records. International journal of medical informatics, Vol. 112 (2018), 59--67. Theodora S Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch Paschalidis, and Wei Shi. 2018. Federated learning of predictive models from federated electronic health records. International journal of medical informatics, Vol. 112 (2018), 59--67."},{"key":"e_1_3_2_1_6_1","volume-title":"Federated meta-learning for recommendation. arXiv preprint arXiv:1802.07876","author":"Chen Fei","year":"2018","unstructured":"Fei Chen , Zhenhua Dong , Zhenguo Li , and Xiuqiang He. 2018. Federated meta-learning for recommendation. arXiv preprint arXiv:1802.07876 ( 2018 ). Fei Chen, Zhenhua Dong, Zhenguo Li, and Xiuqiang He. 2018. Federated meta-learning for recommendation. arXiv preprint arXiv:1802.07876 (2018)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30211-7"},{"key":"e_1_3_2_1_8_1","volume-title":"COVID-19 image data collection. arXiv","author":"Cohen Joseph Paul","year":"2003","unstructured":"Joseph Paul Cohen , Paul Morrison , and Lan Dao . 2020. COVID-19 image data collection. arXiv 2003 .11597 (2020). https:\/\/github.com\/ieee8023\/covid-chestxray-dataset Joseph Paul Cohen, Paul Morrison, and Lan Dao. 2020. COVID-19 image data collection. arXiv 2003.11597 (2020). https:\/\/github.com\/ieee8023\/covid-chestxray-dataset"},{"key":"e_1_3_2_1_9_1","volume-title":"Inverting Gradients--How easy is it to break privacy in federated learning? arXiv preprint arXiv:2003.14053","author":"Geiping Jonas","year":"2020","unstructured":"Jonas Geiping , Hartmut Bauermeister , Hannah Dr\u00f6ge , and Michael Moeller . 2020. Inverting Gradients--How easy is it to break privacy in federated learning? arXiv preprint arXiv:2003.14053 ( 2020 ). Jonas Geiping, Hartmut Bauermeister, Hannah Dr\u00f6ge, and Michael Moeller. 2020. Inverting Gradients--How easy is it to break privacy in federated learning? arXiv preprint arXiv:2003.14053 (2020)."},{"key":"e_1_3_2_1_10_1","volume-title":"Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557","author":"Geyer Robin C","year":"2017","unstructured":"Robin C Geyer , Tassilo Klein , and Moin Nabi . 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 ( 2017 ). Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 (2017)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_13_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Huo Zhouyuan","year":"2018","unstructured":"Zhouyuan Huo , Bin Gu , Heng Huang , 2018 . Decoupled parallel backpropagation with convergence guarantee . In International Conference on Machine Learning. PMLR , 2098--2106. Zhouyuan Huo, Bin Gu, Heng Huang, et al. 2018. Decoupled parallel backpropagation with convergence guarantee. In International Conference on Machine Learning. PMLR, 2098--2106."},{"key":"e_1_3_2_1_14_1","volume-title":"Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al.","author":"Kairouz Peter","year":"2019","unstructured":"Peter Kairouz , H Brendan McMahan , Brendan Avent , Aur\u00e9lien Bellet , Mehdi Bennis , Arjun Nitin Bhagoji , Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2019 . Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019). Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2019. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019)."},{"key":"e_1_3_2_1_15_1","volume-title":"Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882","author":"Kim Yoon","year":"2014","unstructured":"Yoon Kim . 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 ( 2014 ). Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)."},{"key":"e_1_3_2_1_16_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_17_1","unstructured":"Alex Krizhevsky Geoffrey Hinton etal 2009. Learning multiple layers of features from tiny images. (2009).  Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_18_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.  Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105."},{"key":"e_1_3_2_1_19_1","volume-title":"Sinmin Zhang, WenYong Wang, Yousif Abuidris, Waqas Amin, and Jay Kumar.","author":"Kumar Rajesh","year":"2020","unstructured":"Rajesh Kumar , Abdullah Aman Khan , Sinmin Zhang, WenYong Wang, Yousif Abuidris, Waqas Amin, and Jay Kumar. 2020 . Blockchain-federated-learning and de ep learning models for covid-19 detection using ct imaging. arXiv preprint arXiv:2007.06537 (2020). Rajesh Kumar, Abdullah Aman Khan, Sinmin Zhang, WenYong Wang, Yousif Abuidris, Waqas Amin, and Jay Kumar. 2020. Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging. arXiv preprint arXiv:2007.06537 (2020)."},{"key":"e_1_3_2_1_20_1","volume-title":"Eric HY Lau, Jessica Y Wong, et al.","author":"Li Qun","year":"2020","unstructured":"Qun Li , Xuhua Guan , Peng Wu , Xiaoye Wang , Lei Zhou , Yeqing Tong , Ruiqi Ren , Kathy SM Leung , Eric HY Lau, Jessica Y Wong, et al. 2020 . Early transmission dynamics in Wuhan, China, of novel coronavirus--infected pneumonia. New England Journal of Medicine ( 2020). Qun Li, Xuhua Guan, Peng Wu, Xiaoye Wang, Lei Zhou, Yeqing Tong, Ruiqi Ren, Kathy SM Leung, Eric HY Lau, Jessica Y Wong, et al. 2020. Early transmission dynamics in Wuhan, China, of novel coronavirus--infected pneumonia. New England Journal of Medicine (2020)."},{"key":"e_1_3_2_1_21_1","volume-title":"Experiments of federated learning for covid-19 chest x-ray images. arXiv preprint arXiv:2007.05592","author":"Liu Boyi","year":"2020","unstructured":"Boyi Liu , Bingjie Yan , Yize Zhou , Yifan Yang , and Yixian Zhang . 2020. Experiments of federated learning for covid-19 chest x-ray images. arXiv preprint arXiv:2007.05592 ( 2020 ). Boyi Liu, Bingjie Yan, Yize Zhou, Yifan Yang, and Yixian Zhang. 2020. Experiments of federated learning for covid-19 chest x-ray images. arXiv preprint arXiv:2007.05592 (2020)."},{"key":"e_1_3_2_1_22_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial Intelligence and Statistics. 1273--1282.  Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial Intelligence and Statistics. 1273--1282."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178781"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3083187.3083212"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_26_1","volume-title":"Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 1310--1321","author":"Shokri Reza","year":"2015","unstructured":"Reza Shokri and Vitaly Shmatikov . 2015 . Privacy-preserving deep learning . In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 1310--1321 . Reza Shokri and Vitaly Shmatikov. 2015. Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 1310--1321."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759317"},{"key":"e_1_3_2_1_28_1","unstructured":"Virginia Smith Chao-Kai Chiang Maziar Sanjabi and Ameet S Talwalkar. 2017. Federated multi-task learning. In Advances in Neural Information Processing Systems. 4424--4434.  Virginia Smith Chao-Kai Chiang Maziar Sanjabi and Ameet S Talwalkar. 2017. Federated multi-task learning. In Advances in Neural Information Processing Systems. 4424--4434."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2994762"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.1585"},{"key":"e_1_3_2_1_31_1","volume-title":"COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871","author":"Wang Linda","year":"2020","unstructured":"Linda Wang and Alexander Wong . 2020. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871 ( 2020 ). Linda Wang and Alexander Wong. 2020. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871 (2020)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5446"},{"key":"e_1_3_2_1_33_1","volume-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747","author":"Xiao Han","year":"2017","unstructured":"Han Xiao , Kashif Rasul , and Roland Vollgraf . 2017. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 ( 2017 ). Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"e_1_3_2_1_35_1","volume-title":"2019 a. Ouroboros: On Accelerating Training of Transformer-Based Language Models. arXiv preprint arXiv:1909.06695","author":"Yang Qian","year":"2019","unstructured":"Qian Yang , Zhouyuan Huo , Wenlin Wang , Heng Huang , and Lawrence Carin . 2019 a. Ouroboros: On Accelerating Training of Transformer-Based Language Models. arXiv preprint arXiv:1909.06695 ( 2019 ). Qian Yang, Zhouyuan Huo, Wenlin Wang, Heng Huang, and Lawrence Carin. 2019 a. Ouroboros: On Accelerating Training of Transformer-Based Language Models. arXiv preprint arXiv:1909.06695 (2019)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_3_2_1_37_1","unstructured":"Xiang Zhang Junbo Zhao and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Advances in neural information processing systems. 649--657.  Xiang Zhang Junbo Zhao and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Advances in neural information processing systems. 649--657."},{"key":"e_1_3_2_1_38_1","volume-title":"Konda Reddy Mopuri, and Hakan Bilen","author":"Zhao Bo","year":"2020","unstructured":"Bo Zhao , Konda Reddy Mopuri, and Hakan Bilen . 2020 . iDLG: Improved Deep Leakage from Gradients . arXiv preprint arXiv:2001.02610 (2020). Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020. iDLG: Improved Deep Leakage from Gradients. arXiv preprint arXiv:2001.02610 (2020)."},{"key":"e_1_3_2_1_39_1","volume-title":"Federated learning with non-iid data. arXiv preprint arXiv:1806.00582","author":"Zhao Yue","year":"2018","unstructured":"Yue Zhao , Meng Li , Liangzhen Lai , Naveen Suda , Damon Civin , and Vikas Chandra . 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 ( 2018 ). Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 (2018)."},{"key":"e_1_3_2_1_40_1","volume-title":"Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv","author":"Zheng Chuansheng","year":"2020","unstructured":"Chuansheng Zheng , Xianbo Deng , Qing Fu , Qiang Zhou , Jiapei Feng , Hui Ma , Wenyu Liu , and Xinggang Wang . 2020. Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv ( 2020 ). Chuansheng Zheng, Xianbo Deng, Qing Fu, Qiang Zhou, Jiapei Feng, Hui Ma, Wenyu Liu, and Xinggang Wang. 2020. Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv (2020)."},{"key":"e_1_3_2_1_41_1","unstructured":"Ligeng Zhu Zhijian Liu and Song Han. 2019. Deep leakage from gradients. In Advances in Neural Information Processing Systems. 14747--14756.  Ligeng Zhu Zhijian Liu and Song Han. 2019. Deep leakage from gradients. In Advances in Neural Information Processing Systems. 14747--14756."}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467185","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467185","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:27Z","timestamp":1750191507000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467185"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":41,"alternative-id":["10.1145\/3447548.3467185","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467185","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}