{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T06:04:01Z","timestamp":1786169041524,"version":"3.56.0"},"reference-count":123,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T00:00:00Z","timestamp":1622592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Ministry of Education and Research of Germany in the framework of KI-LAB-ITSE","award":["01IS19066"],"award-info":[{"award-number":["01IS19066"]}]},{"name":"HPI Research School on Data Science and Engineering"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2021,6,23]]},"abstract":"<jats:p>Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients\u2019 anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.<\/jats:p>","DOI":"10.1145\/3412357","type":"journal-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T02:47:39Z","timestamp":1622688459000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":273,"title":["Federated Learning in a Medical Context: A Systematic Literature Review"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7824-8872","authenticated-orcid":false,"given":"Bjarne","family":"Pfitzner","sequence":"first","affiliation":[{"name":"University of Potsdam, Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nico","family":"Steckhan","sequence":"additional","affiliation":[{"name":"University of Potsdam, Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bert","family":"Arnrich","sequence":"additional","affiliation":[{"name":"University of Potsdam, Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,6,2]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"Mohammad Mohammadi Amiri and Deniz G\u00fcnd\u00fcz. 2019. Federateds learning over wireless fading channels. CoRR abs\/1907.09769. arXiv:1907.09769 http:\/\/arxiv.org\/abs\/1907.09769.  Mohammad Mohammadi Amiri and Deniz G\u00fcnd\u00fcz. 2019. Federateds learning over wireless fading channels. CoRR abs\/1907.09769. arXiv:1907.09769 http:\/\/arxiv.org\/abs\/1907.09769."},{"key":"e_1_2_2_2_1","volume-title":"Kuan Eeik Tan, and Adrian Flanagan","author":"Muhammad","year":"2019","unstructured":"Muhammad Ammad-ud-din, Elena Ivannikova , Suleiman A. Khan , Were Oyomno , Qiang Fu , Kuan Eeik Tan, and Adrian Flanagan . 2019 . Federated collaborative filtering for privacy-preserving personalized recommendation system. CoRR abs\/1901.09888. arXiv:1901.09888 http:\/\/arxiv.org\/abs\/1901.09888. Muhammad Ammad-ud-din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan. 2019. Federated collaborative filtering for privacy-preserving personalized recommendation system. CoRR abs\/1901.09888. arXiv:1901.09888 http:\/\/arxiv.org\/abs\/1901.09888."},{"key":"e_1_2_2_3_1","volume-title":"How to backdoor federated learning. CoRR abs\/1807.00459","author":"Bagdasaryan Eugene","year":"2018","unstructured":"Eugene Bagdasaryan , Andreas Veit , Yiqing Hua , Deborah Estrin , and Vitaly Shmatikov . 2018. How to backdoor federated learning. CoRR abs\/1807.00459 ( 2018 ). arXiv:1807.00459 http:\/\/arxiv.org\/abs\/1807.00459. Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2018. How to backdoor federated learning. CoRR abs\/1807.00459 (2018). arXiv:1807.00459 http:\/\/arxiv.org\/abs\/1807.00459."},{"key":"e_1_2_2_4_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.)","volume":"97","author":"Bhagoji Arjun Nitin","year":"2019","unstructured":"Arjun Nitin Bhagoji , Supriyo Chakraborty , Prateek Mittal , and Seraphin Calo . 2019 . Analyzing federated learning through an adversarial lens . In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.) , Vol. 97 . PMLR, Long Beach, CA, 634\u2013643. Retrieved from http:\/\/proceedings.mlr.press\/v97\/bhagoji19a.html. Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. 2019. Analyzing federated learning through an adversarial lens. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. PMLR, Long Beach, CA, 634\u2013643. Retrieved from http:\/\/proceedings.mlr.press\/v97\/bhagoji19a.html."},{"key":"e_1_2_2_5_1","volume-title":"Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984","author":"Bhowmick Abhishek","year":"2018","unstructured":"Abhishek Bhowmick , John Duchi , Julien Freudiger , Gaurav Kapoor , and Ryan Rogers . 2018. Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984 ( 2018 ). Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. 2018. Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984 (2018)."},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_2_2_8_1","volume-title":"CoRR abs\/1812.07210","author":"Caldas Sebastian","year":"2018","unstructured":"Sebastian Caldas , Jakub Konecn\u00fd , H. Brendan McMahan , and Ameet Talwalkar . 2018. CoRR abs\/1812.07210 ( 2018 ). CoRR abs\/1812.07210 (2018). arXiv:1812.07210 http:\/\/arxiv.org\/abs\/1812.07210. Sebastian Caldas, Jakub Konecn\u00fd, H. Brendan McMahan, and Ameet Talwalkar. 2018. CoRR abs\/1812.07210 (2018). CoRR abs\/1812.07210 (2018). arXiv:1812.07210 http:\/\/arxiv.org\/abs\/1812.07210."},{"key":"e_1_2_2_9_1","volume-title":"Secure federated matrix factorization. CoRR abs\/1906.05108","author":"Chai Di","year":"2019","unstructured":"Di Chai , Leye Wang , Kai Chen , and Qiang Yang . 2019. Secure federated matrix factorization. CoRR abs\/1906.05108 ( 2019 ). arXiv:1906.05108 http:\/\/arxiv.org\/abs\/1906.05108. Di Chai, Leye Wang, Kai Chen, and Qiang Yang. 2019. Secure federated matrix factorization. CoRR abs\/1906.05108 (2019). arXiv:1906.05108 http:\/\/arxiv.org\/abs\/1906.05108."},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5188-5"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/3042573.3042761"},{"key":"e_1_2_2_12_1","volume-title":"FedHealth: A federated transfer learning framework for wearable healthcare. CoRR abs\/1907.09173","author":"Chen Yiqiang","year":"2019","unstructured":"Yiqiang Chen , Jindong Wang , Chaohui Yu , Wen Gao , and Xin Qin . 2019. FedHealth: A federated transfer learning framework for wearable healthcare. CoRR abs\/1907.09173 ( 2019 ). arXiv:1907.09173 http:\/\/arxiv.org\/abs\/1907.09173. Yiqiang Chen, Jindong Wang, Chaohui Yu, Wen Gao, and Xin Qin. 2019. FedHealth: A federated transfer learning framework for wearable healthcare. CoRR abs\/1907.09173 (2019). arXiv:1907.09173 http:\/\/arxiv.org\/abs\/1907.09173."},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.5555\/2074094.2074100"},{"key":"e_1_2_2_14_1","volume-title":"Buhmann","author":"Corinzia Luca","year":"2019","unstructured":"Luca Corinzia and Joachim M . Buhmann . 2019 . Variational federated multi-task learning. CoRR abs\/1906.06268 (2019). arXiv:1906.06268 http:\/\/arxiv.org\/abs\/1906.06268. Luca Corinzia and Joachim M. Buhmann. 2019. Variational federated multi-task learning. CoRR abs\/1906.06268 (2019). arXiv:1906.06268 http:\/\/arxiv.org\/abs\/1906.06268."},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/646334.687813"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1985.1057074"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"e_1_2_2_18_1","volume-title":"Dancing in the dark: Private multi-party machine learning in an untrusted setting. CoRR abs\/1811.09712","author":"Fung Clement","year":"2018","unstructured":"Clement Fung , Jamie Koerner , Stewart Grant , and Ivan Beschastnikh . 2018. Dancing in the dark: Private multi-party machine learning in an untrusted setting. CoRR abs\/1811.09712 ( 2018 ). arXiv:1811.09712 http:\/\/arxiv.org\/abs\/1811.09712. Clement Fung, Jamie Koerner, Stewart Grant, and Ivan Beschastnikh. 2018. Dancing in the dark: Private multi-party machine learning in an untrusted setting. CoRR abs\/1811.09712 (2018). arXiv:1811.09712 http:\/\/arxiv.org\/abs\/1811.09712."},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"e_1_2_2_20_1","volume-title":"Proceedings of the IEEE Conference on Decision and Control (CDC\u201918)","author":"Gade S.","year":"2018","unstructured":"S. Gade and N. H. Vaidya . 2018. Privacy-preserving distributed learning via obfuscated stochastic gradients . In Proceedings of the IEEE Conference on Decision and Control (CDC\u201918) . 184\u2013191. DOI:DOI:https:\/\/doi.org\/10.1109\/CDC. 2018 .8619133 10.1109\/CDC.2018.8619133 S. Gade and N. H. Vaidya. 2018. Privacy-preserving distributed learning via obfuscated stochastic gradients. In Proceedings of the IEEE Conference on Decision and Control (CDC\u201918). 184\u2013191. DOI:DOI:https:\/\/doi.org\/10.1109\/CDC.2018.8619133"},{"key":"e_1_2_2_21_1","volume-title":"Differentially private federated learning: A client level perspective. CoRR abs\/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. CoRR abs\/1712.07557 ( 2017 ). arXiv:1712.07557 http:\/\/arxiv.org\/abs\/1712.07557. Robin C. Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. CoRR abs\/1712.07557 (2017). arXiv:1712.07557 http:\/\/arxiv.org\/abs\/1712.07557."},{"key":"e_1_2_2_22_1","volume-title":"One-shot federated learning. CoRR abs\/1902.11175","author":"Guha Neel","year":"2019","unstructured":"Neel Guha , Ameet Talwalkar , and Virginia Smith . 2019. One-shot federated learning. CoRR abs\/1902.11175 ( 2019 ). arXiv:1902.11175 http:\/\/arxiv.org\/abs\/1902.11175. Neel Guha, Ameet Talwalkar, and Virginia Smith. 2019. One-shot federated learning. CoRR abs\/1902.11175 (2019). arXiv:1902.11175 http:\/\/arxiv.org\/abs\/1902.11175."},{"key":"e_1_2_2_23_1","volume-title":"Proceedings of the IEEE International Conference on Communications (ICC\u201919)","author":"Hao M.","year":"2019","unstructured":"M. Hao , H. Li , G. Xu , S. Liu , and H. Yang . 2019. Towards efficient and privacy-preserving federated deep learning . In Proceedings of the IEEE International Conference on Communications (ICC\u201919) . 1\u20136. DOI:DOI:https:\/\/doi.org\/10.1109\/ICC. 2019 .8761267 10.1109\/ICC.2019.8761267 M. Hao, H. Li, G. Xu, S. Liu, and H. Yang. 2019. Towards efficient and privacy-preserving federated deep learning. In Proceedings of the IEEE International Conference on Communications (ICC\u201919). 1\u20136. DOI:DOI:https:\/\/doi.org\/10.1109\/ICC.2019.8761267"},{"key":"e_1_2_2_24_1","volume-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. CoRR abs\/1711.10677","author":"Hardy Stephen","year":"2017","unstructured":"Stephen Hardy , Wilko Henecka , Hamish Ivey-Law , Richard Nock , Giorgio Patrini , Guillaume Smith , and Brian Thorne . 2017. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. CoRR abs\/1711.10677 ( 2017 ). arXiv:1711.10677 http:\/\/arxiv.org\/abs\/1711.10677. Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. CoRR abs\/1711.10677 (2017). arXiv:1711.10677 http:\/\/arxiv.org\/abs\/1711.10677."},{"key":"e_1_2_2_25_1","volume-title":"Privacy-preserving classification with secret vector machines. CoRR abs\/1907.03373","author":"Hartmann Valentin","year":"2019","unstructured":"Valentin Hartmann , Konark Modi , Josep M. Pujol , and Robert West . 2019. Privacy-preserving classification with secret vector machines. CoRR abs\/1907.03373 ( 2019 ). arXiv:1907.03373 http:\/\/arxiv.org\/abs\/1907.03373. Valentin Hartmann, Konark Modi, Josep M. Pujol, and Robert West. 2019. Privacy-preserving classification with secret vector machines. CoRR abs\/1907.03373 (2019). arXiv:1907.03373 http:\/\/arxiv.org\/abs\/1907.03373."},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2004.04.005"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559850"},{"key":"e_1_2_2_28_1","volume-title":"Proceedings of the IEEE Global Communications Conference (GLOBECOM\u201918)","author":"Hu B.","year":"2018","unstructured":"B. Hu , Y. Gao , L. Liu , and H. Ma . 2018. Federated region-learning: An edge computing based framework for urban environment sensing . In Proceedings of the IEEE Global Communications Conference (GLOBECOM\u201918) . 1\u20137. DOI:DOI:https:\/\/doi.org\/10.1109\/GLOCOM. 2018 .8647649 10.1109\/GLOCOM.2018.8647649 B. Hu, Y. Gao, L. Liu, and H. Ma. 2018. Federated region-learning: An edge computing based framework for urban environment sensing. In Proceedings of the IEEE Global Communications Conference (GLOBECOM\u201918). 1\u20137. DOI:DOI:https:\/\/doi.org\/10.1109\/GLOCOM.2018.8647649"},{"key":"e_1_2_2_29_1","volume-title":"Advances in Computational Intelligence","author":"Hu Yao","unstructured":"Yao Hu , Xiaoyan Sun , Yang Chen , and Zishuai Lu. 2019. Model and feature aggregation based federated learning for multi-sensor time series trend following . In Advances in Computational Intelligence , Ignacio Rojas, Gonzalo Joya, and Andreu Catala (Eds.). Springer International Publishing , Cham , 233\u2013246. Yao Hu, Xiaoyan Sun, Yang Chen, and Zishuai Lu. 2019. Model and feature aggregation based federated learning for multi-sensor time series trend following. In Advances in Computational Intelligence, Ignacio Rojas, Gonzalo Joya, and Andreu Catala (Eds.). Springer International Publishing, Cham, 233\u2013246."},{"key":"e_1_2_2_30_1","volume-title":"Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. CoRR abs\/1903.09296","author":"Huang Li","year":"2019","unstructured":"Li Huang and Dianbo Liu . 2019. Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. CoRR abs\/1903.09296 ( 2019 ). arXiv:1903.09296 http:\/\/arxiv.org\/abs\/1903.09296. Li Huang and Dianbo Liu. 2019. Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. CoRR abs\/1903.09296 (2019). arXiv:1903.09296 http:\/\/arxiv.org\/abs\/1903.09296."},{"key":"e_1_2_2_31_1","volume-title":"LoAdaBoost: Loss-based adaboost federated machine learning on medical data. CoRR abs\/1811.12629","author":"Huang Li","year":"2018","unstructured":"Li Huang , Yifeng Yin , Zeng Fu , Shifa Zhang , Hao Deng , and Dianbo Liu . 2018. LoAdaBoost: Loss-based adaboost federated machine learning on medical data. CoRR abs\/1811.12629 ( 2018 ). arXiv:1811.12629 http:\/\/arxiv.org\/abs\/1811.12629. Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, and Dianbo Liu. 2018. LoAdaBoost: Loss-based adaboost federated machine learning on medical data. CoRR abs\/1811.12629 (2018). arXiv:1811.12629 http:\/\/arxiv.org\/abs\/1811.12629."},{"key":"e_1_2_2_32_1","volume-title":"Learning private neural language modeling with attentive aggregation. CoRR abs\/1812.07108","author":"Ji Shaoxiong","year":"2018","unstructured":"Shaoxiong Ji , Shirui Pan , Guodong Long , Xue Li , Jing Jiang , and Zi Huang . 2018. Learning private neural language modeling with attentive aggregation. CoRR abs\/1812.07108 ( 2018 ). arXiv:1812.07108 http:\/\/arxiv.org\/abs\/1812.07108. Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, and Zi Huang. 2018. Learning private neural language modeling with attentive aggregation. CoRR abs\/1812.07108 (2018). arXiv:1812.07108 http:\/\/arxiv.org\/abs\/1812.07108."},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756123.1756146"},{"key":"e_1_2_2_34_1","volume-title":"Federated optimization: Distributed optimization beyond the datacenter. CoRR abs\/1511.03575","author":"Konecn\u00fd Jakub","year":"2015","unstructured":"Jakub Konecn\u00fd , Brendan McMahan , and Daniel Ramage . 2015. Federated optimization: Distributed optimization beyond the datacenter. CoRR abs\/1511.03575 ( 2015 ). arXiv:1511.03575 http:\/\/arxiv.org\/abs\/1511.03575. Jakub Konecn\u00fd, Brendan McMahan, and Daniel Ramage. 2015. Federated optimization: Distributed optimization beyond the datacenter. CoRR abs\/1511.03575 (2015). arXiv:1511.03575 http:\/\/arxiv.org\/abs\/1511.03575."},{"key":"e_1_2_2_35_1","volume-title":"Federated optimization: Distributed machine learning for on-device intelligence. CoRR abs\/1610.02527","author":"Konecn\u00fd Jakub","year":"2016","unstructured":"Jakub Konecn\u00fd , H. Brendan McMahan , Daniel Ramage , and Peter Richt\u00e1rik . 2016. Federated optimization: Distributed machine learning for on-device intelligence. CoRR abs\/1610.02527 ( 2016 ). arXiv:1610.02527 http:\/\/arxiv.org\/abs\/1610.02527. Jakub Konecn\u00fd, H. Brendan McMahan, Daniel Ramage, and Peter Richt\u00e1rik. 2016. Federated optimization: Distributed machine learning for on-device intelligence. CoRR abs\/1610.02527 (2016). arXiv:1610.02527 http:\/\/arxiv.org\/abs\/1610.02527."},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.5555\/578628"},{"key":"e_1_2_2_37_1","volume-title":"Randomized distributed mean estimation: Accuracy vs. communication. CoRR abs\/1611.07555","author":"Konecn\u00fd Jakub","year":"2016","unstructured":"Jakub Konecn\u00fd and Peter Richt\u00e1rik . 2016. Randomized distributed mean estimation: Accuracy vs. communication. CoRR abs\/1611.07555 ( 2016 ). arXiv:1611.07555 http:\/\/arxiv.org\/abs\/1611.07555. Jakub Konecn\u00fd and Peter Richt\u00e1rik. 2016. Randomized distributed mean estimation: Accuracy vs. communication. CoRR abs\/1611.07555 (2016). arXiv:1611.07555 http:\/\/arxiv.org\/abs\/1611.07555."},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1026543900054"},{"key":"e_1_2_2_39_1","volume-title":"Privacy-preserving patient similarity learning in a federated environment: Development and analysis. JMIR Med. Inform. 6, 2 (13","author":"Lee Junghye","year":"2018","unstructured":"Junghye Lee , Jimeng Sun , Fei Wang , Shuang Wang , Chi-Hyuck Jun , and Xiaoqian Jiang . 2018. Privacy-preserving patient similarity learning in a federated environment: Development and analysis. JMIR Med. Inform. 6, 2 (13 Apr. 2018 ), e20. DOI:DOI:https:\/\/doi.org\/10.2196\/medinform.7744 10.2196\/medinform.7744 Junghye Lee, Jimeng Sun, Fei Wang, Shuang Wang, Chi-Hyuck Jun, and Xiaoqian Jiang. 2018. Privacy-preserving patient similarity learning in a federated environment: Development and analysis. JMIR Med. Inform. 6, 2 (13 Apr. 2018), e20. DOI:DOI:https:\/\/doi.org\/10.2196\/medinform.7744"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/359168.359176"},{"key":"e_1_2_2_41_1","volume-title":"Fair resource allocation in federated learning. CoRR abs\/1905.10497","author":"Li Tian","year":"2019","unstructured":"Tian Li , Maziar Sanjabi , and Virginia Smith . 2019. Fair resource allocation in federated learning. CoRR abs\/1905.10497 ( 2019 ). arXiv:1905.10497 http:\/\/arxiv.org\/abs\/1905.10497. Tian Li, Maziar Sanjabi, and Virginia Smith. 2019. Fair resource allocation in federated learning. CoRR abs\/1905.10497 (2019). arXiv:1905.10497 http:\/\/arxiv.org\/abs\/1905.10497."},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocv146"},{"key":"e_1_2_2_43_1","volume-title":"Dally","author":"Lin Yujun","year":"2017","unstructured":"Yujun Lin , Song Han , Huizi Mao , Yu Wang , and William J . Dally . 2017 . Deep gradient compression: Reducing the communication bandwidth for distributed training. CoRR abs\/1712.01887 (2017). Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J. Dally. 2017. Deep gradient compression: Reducing the communication bandwidth for distributed training. CoRR abs\/1712.01887 (2017)."},{"key":"e_1_2_2_44_1","volume-title":"Mandl","author":"Liu Dianbo","year":"2018","unstructured":"Dianbo Liu , Timothy Miller , Raheel Sayeed , and Kenneth D . Mandl . 2018 . FADL : Federated-autonomous deep learning for distributed electronic health record. CoRR abs\/1811.11400 (2018). arXiv:1811.11400 http:\/\/arxiv.org\/abs\/1811.11400. Dianbo Liu, Timothy Miller, Raheel Sayeed, and Kenneth D. Mandl. 2018. FADL: Federated-autonomous deep learning for distributed electronic health record. CoRR abs\/1811.11400 (2018). arXiv:1811.11400 http:\/\/arxiv.org\/abs\/1811.11400."},{"key":"e_1_2_2_45_1","volume-title":"Secure federated transfer learning. CoRR abs\/1812.03337","author":"Liu Yang","year":"2018","unstructured":"Yang Liu , Tianjian Chen , and Qiang Yang . 2018. Secure federated transfer learning. CoRR abs\/1812.03337 ( 2018 ). arXiv:1812.03337 http:\/\/arxiv.org\/abs\/1812.03337. Yang Liu, Tianjian Chen, and Qiang Yang. 2018. Secure federated transfer learning. CoRR abs\/1812.03337 (2018). arXiv:1812.03337 http:\/\/arxiv.org\/abs\/1812.03337."},{"key":"e_1_2_2_46_1","volume-title":"Deng","author":"Liu Yang","year":"2019","unstructured":"Yang Liu , Zhuo Ma , Ximeng Liu , Siqi Ma , Surya Nepal , and Robert H . Deng . 2019 . Boosting privately: Privacy-preserving federated extreme boosting for mobile crowdsensing. CoRR abs\/1907.10218 (2019). arXiv:1907.10218 http:\/\/arxiv.org\/abs\/1907.10218. Yang Liu, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, and Robert H. Deng. 2019. Boosting privately: Privacy-preserving federated extreme boosting for mobile crowdsensing. CoRR abs\/1907.10218 (2019). arXiv:1907.10218 http:\/\/arxiv.org\/abs\/1907.10218."},{"key":"e_1_2_2_47_1","volume-title":"Federated learning of deep networks using model averaging. CoRR abs\/1602.05629","author":"McMahan H. Brendan","year":"2016","unstructured":"H. Brendan McMahan , Eider Moore , Daniel Ramage , and Blaise Ag\u00fcera y Arcas . 2016. Federated learning of deep networks using model averaging. CoRR abs\/1602.05629 ( 2016 ). arXiv:1602.05629 http:\/\/arxiv.org\/abs\/1602.05629. H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Ag\u00fcera y Arcas. 2016. Federated learning of deep networks using model averaging. CoRR abs\/1602.05629 (2016). arXiv:1602.05629 http:\/\/arxiv.org\/abs\/1602.05629."},{"key":"e_1_2_2_48_1","volume-title":"Learning differentially private language models without losing accuracy. CoRR abs\/1710.06963","author":"McMahan H. Brendan","year":"2017","unstructured":"H. Brendan McMahan , Daniel Ramage , Kunal Talwar , and Li Zhang . 2017. Learning differentially private language models without losing accuracy. CoRR abs\/1710.06963 ( 2017 ). arXiv:1710.06963 http:\/\/arxiv.org\/abs\/1710.06963. H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2017. Learning differentially private language models without losing accuracy. CoRR abs\/1710.06963 (2017). arXiv:1710.06963 http:\/\/arxiv.org\/abs\/1710.06963."},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"e_1_2_2_50_1","volume-title":"Agnostic federated learning. CoRR abs\/1902.00146","author":"Mohri Mehryar","year":"2019","unstructured":"Mehryar Mohri , Gary Sivek , and Ananda Theertha Suresh . 2019. Agnostic federated learning. CoRR abs\/1902.00146 ( 2019 ). arXiv:1902.00146 http:\/\/arxiv.org\/abs\/1902.00146. Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh. 2019. Agnostic federated learning. CoRR abs\/1902.00146 (2019). arXiv:1902.00146 http:\/\/arxiv.org\/abs\/1902.00146."},{"key":"e_1_2_2_51_1","volume-title":"Proceedings of the IEEE Symposium on Security and Privacy (SP\u201919)","author":"Nasr M.","year":"2019","unstructured":"M. Nasr , R. Shokri , and A. Houmansadr . 2019. Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning . In Proceedings of the IEEE Symposium on Security and Privacy (SP\u201919) . IEEE Computer Society, Los Alamitos, CA, 1021\u20131035. DOI:DOI:https:\/\/doi.org\/10.1109\/SP. 2019 .00065 10.1109\/SP.2019.00065 M. Nasr, R. Shokri, and A. Houmansadr. 2019. Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In Proceedings of the IEEE Symposium on Security and Privacy (SP\u201919). IEEE Computer Society, Los Alamitos, CA, 1021\u20131035. DOI:DOI:https:\/\/doi.org\/10.1109\/SP.2019.00065"},{"key":"e_1_2_2_52_1","volume-title":"Client selection for federated learning with heterogeneous resources in mobile edge. CoRR abs\/1804.08333","author":"Nishio Takayuki","year":"2018","unstructured":"Takayuki Nishio and Ryo Yonetani . 2018. Client selection for federated learning with heterogeneous resources in mobile edge. CoRR abs\/1804.08333 ( 2018 ). arXiv:1804.08333 http:\/\/arxiv.org\/abs\/1804.08333. Takayuki Nishio and Ryo Yonetani. 2018. Client selection for federated learning with heterogeneous resources in mobile edge. CoRR abs\/1804.08333 (2018). arXiv:1804.08333 http:\/\/arxiv.org\/abs\/1804.08333."},{"key":"e_1_2_2_53_1","volume-title":"Entity resolution and federated learning get a federated resolution. CoRR abs\/1803.04035","author":"Nock Richard","year":"2018","unstructured":"Richard Nock , Stephen Hardy , Wilko Henecka , Hamish Ivey-Law , Giorgio Patrini , Guillaume Smith , and Brian Thorne . 2018. Entity resolution and federated learning get a federated resolution. CoRR abs\/1803.04035 ( 2018 ). arXiv:1803.04035 http:\/\/arxiv.org\/abs\/1803.04035. Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2018. Entity resolution and federated learning get a federated resolution. CoRR abs\/1803.04035 (2018). arXiv:1803.04035 http:\/\/arxiv.org\/abs\/1803.04035."},{"key":"e_1_2_2_54_1","volume-title":"Bernt Schiele, and Mario Fritz.","author":"Orekondy Tribhuvanesh","year":"2018","unstructured":"Tribhuvanesh Orekondy , Seong Joon Oh , Bernt Schiele, and Mario Fritz. 2018 . Understanding and controlling user linkability in decentralized learning. CoRR abs\/1805.05838 (2018). arXiv:1812.06127 http:\/\/arxiv.org\/abs\/1812.06127. Tribhuvanesh Orekondy, Seong Joon Oh, Bernt Schiele, and Mario Fritz. 2018. Understanding and controlling user linkability in decentralized learning. CoRR abs\/1805.05838 (2018). arXiv:1812.06127 http:\/\/arxiv.org\/abs\/1812.06127."},{"key":"e_1_2_2_55_1","volume-title":"Federated optimization for heterogeneous networks. CoRR abs\/1812.06127","author":"Sahu Anit Kumar","year":"2018","unstructured":"Anit Kumar Sahu , Tian Li , Maziar Sanjabi , Manzil Zaheer , Ameet Talwalkar , and Virginia Smith . 2018. Federated optimization for heterogeneous networks. CoRR abs\/1812.06127 ( 2018 ). arXiv:1812.06127 http:\/\/arxiv.org\/abs\/1812.06127. Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith. 2018. Federated optimization for heterogeneous networks. CoRR abs\/1812.06127 (2018). arXiv:1812.06127 http:\/\/arxiv.org\/abs\/1812.06127."},{"key":"e_1_2_2_56_1","volume-title":"A federated filtering framework for Internet of medical things. CoRR abs\/1905.01138","author":"Sanyal Sunny","year":"2019","unstructured":"Sunny Sanyal , Dapeng Wu , and Boubakr Nour . 2019. A federated filtering framework for Internet of medical things. CoRR abs\/1905.01138 ( 2019 ). arXiv:1905.01138 http:\/\/arxiv.org\/abs\/1905.01138. Sunny Sanyal, Dapeng Wu, and Boubakr Nour. 2019. A federated filtering framework for Internet of medical things. CoRR abs\/1905.01138 (2019). arXiv:1905.01138 http:\/\/arxiv.org\/abs\/1905.01138."},{"key":"e_1_2_2_57_1","volume-title":"Robust and communication-efficient federated learning from non-IID data. CoRR abs\/1903.02891","author":"Sattler Felix","year":"2019","unstructured":"Felix Sattler , Simon Wiedemann , Klaus-Robert M\u00fcller , and Wojciech Samek . 2019. Robust and communication-efficient federated learning from non-IID data. CoRR abs\/1903.02891 ( 2019 ). arXiv:1903.02891 http:\/\/arxiv.org\/abs\/1903.02891. Felix Sattler, Simon Wiedemann, Klaus-Robert M\u00fcller, and Wojciech Samek. 2019. Robust and communication-efficient federated learning from non-IID data. CoRR abs\/1903.02891 (2019). arXiv:1903.02891 http:\/\/arxiv.org\/abs\/1903.02891."},{"key":"e_1_2_2_58_1","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Alessandro Crimi, Spyridon Bakas, Hugo Kuijf, Farahani Keyvan, Mauricio Reyes, and Theo van Walsum (Eds.)","author":"Sheller Micah J.","unstructured":"Micah J. Sheller , G. Anthony Reina , Brandon Edwards , Jason Martin , and Spyridon Bakas . 2019. Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation . In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Alessandro Crimi, Spyridon Bakas, Hugo Kuijf, Farahani Keyvan, Mauricio Reyes, and Theo van Walsum (Eds.) . Springer International Publishing , Cham , 92\u2013104. Micah J. Sheller, G. Anthony Reina, Brandon Edwards, Jason Martin, and Spyridon Bakas. 2019. Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Alessandro Crimi, Spyridon Bakas, Hugo Kuijf, Farahani Keyvan, Mauricio Reyes, and Theo van Walsum (Eds.). Springer International Publishing, Cham, 92\u2013104."},{"key":"e_1_2_2_59_1","first-page":"I","article-title":"Federated multi-task learning","volume":"30","author":"Smith Virginia","year":"2017","unstructured":"Virginia Smith , Chao-Kai Chiang , Maziar Sanjabi , and Ameet S. Talwalkar . 2017 . Federated multi-task learning . In Advances in Neural Information Processing Systems 30 , I . Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 4424\u20134434. Retrieved from http:\/\/papers.nips.cc\/paper\/7029-federated-multi-task-learning.pdf. Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S. Talwalkar. 2017. Federated multi-task learning. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 4424\u20134434. Retrieved from http:\/\/papers.nips.cc\/paper\/7029-federated-multi-task-learning.pdf.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"#cr-split#-e_1_2_2_60_1.1","doi-asserted-by":"crossref","unstructured":"K. Sozinov V. Vlassov and S. Girdzijauskas. 2018. Human activity recognition using federated learning. In Proceedings of the IEEE International Conference on Parallel Distributed Processing with Applications Ubiquitous Computing Communications Big Data Cloud Computing Social Computing Networking Sustainable Computing Communications (ISPA\/IUCC\/BDCloud\/SocialCom\/SustainCom'18). 1103-1111. DOI:DOI:https:\/\/doi.org\/10.1109\/BDCloud.2018.00164 10.1109\/BDCloud.2018.00164","DOI":"10.1109\/BDCloud.2018.00164"},{"key":"#cr-split#-e_1_2_2_60_1.2","doi-asserted-by":"crossref","unstructured":"K. Sozinov V. Vlassov and S. Girdzijauskas. 2018. Human activity recognition using federated learning. In Proceedings of the IEEE International Conference on Parallel Distributed Processing with Applications Ubiquitous Computing Communications Big Data Cloud Computing Social Computing Networking Sustainable Computing Communications (ISPA\/IUCC\/BDCloud\/SocialCom\/SustainCom'18). 1103-1111. DOI:DOI:https:\/\/doi.org\/10.1109\/BDCloud.2018.00164","DOI":"10.1109\/BDCloud.2018.00164"},{"key":"e_1_2_2_61_1","volume-title":"High dimensional restrictive federated model selection with multi-objective Bayesian optimization over shifted distributions. CoRR abs\/1902.08999","author":"Sun Xudong","year":"2019","unstructured":"Xudong Sun , Andrea Bommert , Florian Pfisterer , J\u00f6rg Rahnenf\u00fchrer , Michel Lang , and Bernd Bischl . 2019. High dimensional restrictive federated model selection with multi-objective Bayesian optimization over shifted distributions. CoRR abs\/1902.08999 ( 2019 ). arXiv:1902.08999 http:\/\/arxiv.org\/abs\/1902.08999. Xudong Sun, Andrea Bommert, Florian Pfisterer, J\u00f6rg Rahnenf\u00fchrer, Michel Lang, and Bernd Bischl. 2019. High dimensional restrictive federated model selection with multi-objective Bayesian optimization over shifted distributions. CoRR abs\/1902.08999 (2019). arXiv:1902.08999 http:\/\/arxiv.org\/abs\/1902.08999."},{"key":"e_1_2_2_62_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.)","volume":"70","author":"Suresh Ananda Theertha","year":"2017","unstructured":"Ananda Theertha Suresh , Felix X. Yu , Sanjiv Kumar , and H. Brendan McMahan . 2017 . Distributed mean estimation with limited communication . In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.) , Vol. 70 . PMLR, International Convention Centre, Sydney, Australia, 3329\u20133337. Retrieved from http:\/\/proceedings.mlr.press\/v70\/suresh17a.html. Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, and H. Brendan McMahan. 2017. Distributed mean estimation with limited communication. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.), Vol. 70. PMLR, International Convention Centre, Sydney, Australia, 3329\u20133337. Retrieved from http:\/\/proceedings.mlr.press\/v70\/suresh17a.html."},{"key":"e_1_2_2_63_1","volume-title":"Proceedings of the IEEE Conference on Computer Communications (INFOCOM\u201919)","author":"Tran N. H.","year":"2019","unstructured":"N. H. Tran , W. Bao , A. Zomaya , M. N. H. Nguyen , and C. S. Hong . 2019. Federated learning over wireless networks: Optimization model design and analysis . In Proceedings of the IEEE Conference on Computer Communications (INFOCOM\u201919) . 1387\u20131395. DOI:DOI:https:\/\/doi.org\/10.1109\/INFOCOM. 2019 .8737464 10.1109\/INFOCOM.2019.8737464 N. H. Tran, W. Bao, A. Zomaya, M. N. H. Nguyen, and C. S. Hong. 2019. Federated learning over wireless networks: Optimization model design and analysis. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM\u201919). 1387\u20131395. DOI:DOI:https:\/\/doi.org\/10.1109\/INFOCOM.2019.8737464"},{"key":"e_1_2_2_64_1","volume-title":"A hybrid approach to privacy-preserving federated learning. CoRR abs\/1812.03224","author":"Truex Stacey","year":"2018","unstructured":"Stacey Truex , Nathalie Baracaldo , Ali Anwar , Thomas Steinke , Heiko Ludwig , and Rui Zhang . 2018. A hybrid approach to privacy-preserving federated learning. CoRR abs\/1812.03224 ( 2018 ). arXiv:1812.03224 http:\/\/arxiv.org\/abs\/1812.03224. Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, and Rui Zhang. 2018. A hybrid approach to privacy-preserving federated learning. CoRR abs\/1812.03224 (2018). arXiv:1812.03224 http:\/\/arxiv.org\/abs\/1812.03224."},{"key":"e_1_2_2_65_1","volume-title":"Split learning for health: Distributed deep learning without sharing raw patient data. CoRR abs\/1812.00564","author":"Vepakomma Praneeth","year":"2018","unstructured":"Praneeth Vepakomma , Otkrist Gupta , Tristan Swedish , and Ramesh Raskar . 2018. Split learning for health: Distributed deep learning without sharing raw patient data. CoRR abs\/1812.00564 ( 2018 ). Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar. 2018. Split learning for health: Distributed deep learning without sharing raw patient data. CoRR abs\/1812.00564 (2018)."},{"key":"e_1_2_2_66_1","volume-title":"Proceedings of the IEEE 38th International Conference on Distributed Computing Systems (ICDCS\u201918)","author":"Wang J.","year":"2018","unstructured":"J. Wang , B. Cao , P. Yu , L. Sun , W. Bao , and X. Zhu . 2018. Deep learning towards mobile applications . In Proceedings of the IEEE 38th International Conference on Distributed Computing Systems (ICDCS\u201918) . 1385\u20131393. DOI:DOI:https:\/\/doi.org\/10.1109\/ICDCS. 2018 .00139 10.1109\/ICDCS.2018.00139 J. Wang, B. Cao, P. Yu, L. Sun, W. Bao, and X. Zhu. 2018. Deep learning towards mobile applications. In Proceedings of the IEEE 38th International Conference on Distributed Computing Systems (ICDCS\u201918). 1385\u20131393. DOI:DOI:https:\/\/doi.org\/10.1109\/ICDCS.2018.00139"},{"key":"e_1_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2019.00099"},{"key":"e_1_2_2_68_1","volume-title":"Adaptive federated learning in resource constrained edge computing systems. CoRR abs\/1804.05271","author":"Wang Shiqiang","year":"2018","unstructured":"Shiqiang Wang , Tiffany Tuor , Theodoros Salonidis , Kin K. Leung , Christian Makaya , Ting He , and Kevin Chan . 2018. Adaptive federated learning in resource constrained edge computing systems. CoRR abs\/1804.05271 ( 2018 ). Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, and Kevin Chan. 2018. Adaptive federated learning in resource constrained edge computing systems. CoRR abs\/1804.05271 (2018)."},{"key":"e_1_2_2_69_1","volume-title":"Beyond inferring class representatives: User-level privacy leakage from federated learning. CoRR abs\/1812.00535","author":"Wang Zhibo","year":"2018","unstructured":"Zhibo Wang , Mengkai Song , Zhifei Zhang , Yang Song , Qian Wang , and Hairong Qi. 2018. Beyond inferring class representatives: User-level privacy leakage from federated learning. CoRR abs\/1812.00535 ( 2018 ). arXiv:1812.00535 http:\/\/arxiv.org\/abs\/1812.00535. Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi. 2018. Beyond inferring class representatives: User-level privacy leakage from federated learning. CoRR abs\/1812.00535 (2018). arXiv:1812.00535 http:\/\/arxiv.org\/abs\/1812.00535."},{"key":"e_1_2_2_70_1","volume-title":"Asynchronous federated optimization. CoRR abs\/1903.03934","author":"Xie Cong","year":"2019","unstructured":"Cong Xie , Sanmi Koyejo , and Indranil Gupta . 2019. Asynchronous federated optimization. CoRR abs\/1903.03934 ( 2019 ). arXiv:1903.03934 http:\/\/arxiv.org\/abs\/1903.03934. Cong Xie, Sanmi Koyejo, and Indranil Gupta. 2019. Asynchronous federated optimization. CoRR abs\/1903.03934 (2019). arXiv:1903.03934 http:\/\/arxiv.org\/abs\/1903.03934."},{"key":"e_1_2_2_71_1","volume-title":"Collaborative deep learning across multiple data centers. CoRR abs\/1810.06877","author":"Xu Kele","year":"2018","unstructured":"Kele Xu , Haibo Mi , Dawei Feng , Huaimin Wang , Chuan Chen , Zibin Zheng , and Xu Lan . 2018. Collaborative deep learning across multiple data centers. CoRR abs\/1810.06877 ( 2018 ). arXiv:1810.06877 http:\/\/arxiv.org\/abs\/1810.06877. Kele Xu, Haibo Mi, Dawei Feng, Huaimin Wang, Chuan Chen, Zibin Zheng, and Xu Lan. 2018. Collaborative deep learning across multiple data centers. CoRR abs\/1810.06877 (2018). arXiv:1810.06877 http:\/\/arxiv.org\/abs\/1810.06877."},{"key":"e_1_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3321408.3323080"},{"key":"e_1_2_2_73_1","volume-title":"Federated learning via over-the-air computation. CoRR abs\/1812.11750","author":"Yang Kai","year":"2018","unstructured":"Kai Yang , Tao Jiang , Yuanming Shi , and Zhi Ding . 2018. Federated learning via over-the-air computation. CoRR abs\/1812.11750 ( 2018 ). arXiv:1812.11750 http:\/\/arxiv.org\/abs\/1812.11750. Kai Yang, Tao Jiang, Yuanming Shi, and Zhi Ding. 2018. Federated learning via over-the-air computation. CoRR abs\/1812.11750 (2018). arXiv:1812.11750 http:\/\/arxiv.org\/abs\/1812.11750."},{"key":"e_1_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_2_2_75_1","volume-title":"Proceedings of the IEEE Visual Communications and Image Processing (VCIP\u201918)","author":"Yao X.","year":"2018","unstructured":"X. Yao , C. Huang , and L. Sun . 2018. Two-stream federated learning: Reduce the communication costs . In Proceedings of the IEEE Visual Communications and Image Processing (VCIP\u201918) . 1\u20134. DOI:DOI:https:\/\/doi.org\/10.1109\/VCIP. 2018 .8698609 10.1109\/VCIP.2018.8698609 X. Yao, C. Huang, and L. Sun. 2018. Two-stream federated learning: Reduce the communication costs. In Proceedings of the IEEE Visual Communications and Image Processing (VCIP\u201918). 1\u20134. DOI:DOI:https:\/\/doi.org\/10.1109\/VCIP.2018.8698609"},{"key":"e_1_2_2_76_1","volume-title":"Proceedings of the IEEE 37th International Conference on Distributed Computing Systems (ICDCS\u201917)","author":"Zhang X.","year":"2017","unstructured":"X. Zhang , S. Ji , H. Wang , and T. Wang . 2017. Private, yet practical, multiparty deep learning . In Proceedings of the IEEE 37th International Conference on Distributed Computing Systems (ICDCS\u201917) . 1442\u20131452. DOI:DOI:https:\/\/doi.org\/10.1109\/ICDCS. 2017 .215 10.1109\/ICDCS.2017.215 X. Zhang, S. Ji, H. Wang, and T. Wang. 2017. Private, yet practical, multiparty deep learning. In Proceedings of the IEEE 37th International Conference on Distributed Computing Systems (ICDCS\u201917). 1442\u20131452. DOI:DOI:https:\/\/doi.org\/10.1109\/ICDCS.2017.215"},{"key":"e_1_2_2_77_1","volume-title":"Federated learning with non-IID data. CoRR abs\/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. CoRR abs\/1806.00582 ( 2018 ). arXiv:1806.00582 http:\/\/arxiv.org\/abs\/1806.00582. Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018. Federated learning with non-IID data. CoRR abs\/1806.00582 (2018). arXiv:1806.00582 http:\/\/arxiv.org\/abs\/1806.00582."},{"key":"#cr-split#-e_1_2_2_78_1.1","doi-asserted-by":"crossref","unstructured":"W. Zhou Y. Li S. Chen and B. Ding. 2018. Real-time data processing architecture for multi-robots based on differential federated learning. In Proceedings of the IEEE SmartWorld Ubiquitous Intelligence Computing Advanced Trusted Computing Scalable Computing Communications Cloud Big Data Computing Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI'18). 462-471. DOI:DOI:https:\/\/doi.org\/10.1109\/SmartWorld.2018.00106 10.1109\/SmartWorld.2018.00106","DOI":"10.1109\/SmartWorld.2018.00106"},{"key":"#cr-split#-e_1_2_2_78_1.2","doi-asserted-by":"crossref","unstructured":"W. Zhou Y. Li S. Chen and B. Ding. 2018. Real-time data processing architecture for multi-robots based on differential federated learning. In Proceedings of the IEEE SmartWorld Ubiquitous Intelligence Computing Advanced Trusted Computing Scalable Computing Communications Cloud Big Data Computing Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI'18). 462-471. DOI:DOI:https:\/\/doi.org\/10.1109\/SmartWorld.2018.00106","DOI":"10.1109\/SmartWorld.2018.00106"},{"key":"e_1_2_2_79_1","volume-title":"Multi-objective evolutionary federated learning. CoRR abs\/1812.07478","author":"Zhu Hangyu","year":"2018","unstructured":"Hangyu Zhu and Yaochu Jin . 2018. Multi-objective evolutionary federated learning. CoRR abs\/1812.07478 ( 2018 ). arXiv:1812.07478 http:\/\/arxiv.org\/abs\/1812.07478. Hangyu Zhu and Yaochu Jin. 2018. Multi-objective evolutionary federated learning. CoRR abs\/1812.07478 (2018). arXiv:1812.07478 http:\/\/arxiv.org\/abs\/1812.07478."},{"key":"#cr-split#-e_1_2_2_80_1.1","doi-asserted-by":"crossref","unstructured":"Y. Zou S. Feng D. Niyato Y. Jiao S. Gong and W. Cheng. 2019. Mobile device training strategies in federated learning: An evolutionary game approach. In Proceedings of the International Conference on Internet of Things (iThings'19) and IEEE Green Computing and Communications (GreenCom'19) and IEEE Cyber Physical and Social Computing (CPSCom'19) and IEEE Smart Data (SmartData'19). 874-879. DOI:DOI:https:\/\/doi.org\/10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00157 10.1109\/iThings","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00157"},{"key":"#cr-split#-e_1_2_2_80_1.2","doi-asserted-by":"crossref","unstructured":"Y. Zou S. Feng D. Niyato Y. Jiao S. Gong and W. Cheng. 2019. Mobile device training strategies in federated learning: An evolutionary game approach. In Proceedings of the International Conference on Internet of Things (iThings'19) and IEEE Green Computing and Communications (GreenCom'19) and IEEE Cyber Physical and Social Computing (CPSCom'19) and IEEE Smart Data (SmartData'19). 874-879. DOI:DOI:https:\/\/doi.org\/10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00157","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00157"},{"key":"e_1_2_3_2_1","unstructured":"[n.d.]. DICOM Standard. Retrieved from https:\/\/www.dicomstandard.org\/.  [n.d.]. DICOM Standard. Retrieved from https:\/\/www.dicomstandard.org\/."},{"key":"e_1_2_3_3_1","unstructured":"[n.d.]. eICU Collaborative Research Database. Retrieved from https:\/\/eicu-crd.mit.edu\/.  [n.d.]. eICU Collaborative Research Database. Retrieved from https:\/\/eicu-crd.mit.edu\/."},{"key":"e_1_2_3_4_1","unstructured":"[n.d.]. Index - FHIR v4.0.1. Retrieved from https:\/\/www.hl7.org\/fhir\/.  [n.d.]. Index - FHIR v4.0.1. Retrieved from https:\/\/www.hl7.org\/fhir\/."},{"key":"e_1_2_3_5_1","unstructured":"[n.d.]. LEAF. Retrieved from https:\/\/leaf.cmu.edu\/.  [n.d.]. LEAF. Retrieved from https:\/\/leaf.cmu.edu\/."},{"key":"e_1_2_3_6_1","unstructured":"[n.d.]. MHEALTH Data Set. Retrieved from https:\/\/archive.ics.uci.edu\/ml\/datasets\/MHEALTH+Dataset.  [n.d.]. MHEALTH Data Set. Retrieved from https:\/\/archive.ics.uci.edu\/ml\/datasets\/MHEALTH+Dataset."},{"key":"e_1_2_3_7_1","unstructured":"[n.d.]. MIMIC Critical Care Database. Retrieved from https:\/\/mimic.physionet.org\/.  [n.d.]. MIMIC Critical Care Database. Retrieved from https:\/\/mimic.physionet.org\/."},{"key":"e_1_2_3_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_2_3_9_1","volume-title":"Free DICOM de-identification tools in clinical research: Functioning and safety of patient privacy. Eur. Radiol. 25, 12 (12","author":"Aryanto K. Y. E.","year":"2015","unstructured":"K. Y. E. Aryanto , M. Oudkerk , and P. M. A. van Ooijen . 2015. Free DICOM de-identification tools in clinical research: Functioning and safety of patient privacy. Eur. Radiol. 25, 12 (12 2015 ), 3685\u20133695. K. Y. E. Aryanto, M. Oudkerk, and P. M. A. van Ooijen. 2015. Free DICOM de-identification tools in clinical research: Functioning and safety of patient privacy. Eur. Radiol. 25, 12 (12 2015), 3685\u20133695."},{"key":"e_1_2_3_10_1","volume-title":"Guidelines for performing systematic literature reviews in software engineering. 2 (01","author":"Kitchenham B. A.","year":"2007","unstructured":"B. A. Kitchenham and Stuart Charters . 2007. Guidelines for performing systematic literature reviews in software engineering. 2 (01 2007 ). B. A. Kitchenham and Stuart Charters. 2007. Guidelines for performing systematic literature reviews in software engineering. 2 (01 2007)."},{"key":"e_1_2_3_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5188-5"},{"key":"e_1_2_3_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/3042573.3042761"},{"key":"e_1_2_3_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.07.023"},{"key":"e_1_2_3_14_1","volume-title":"Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI\u201998)","author":"Breese John S.","year":"1998","unstructured":"John S. Breese , David Heckerman , and Carl Kadie . 1998 . Empirical analysis of predictive algorithms for collaborative filtering . In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI\u201998) . Morgan Kaufmann Publishers Inc., San Francisco, CA, 43\u201352. Retrieved from http:\/\/dl.acm.org\/citation.cfm?id= 2074094.2074100. John S. Breese, David Heckerman, and Carl Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI\u201998). Morgan Kaufmann Publishers Inc., San Francisco, CA, 43\u201352. Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=2074094.2074100."},{"key":"e_1_2_3_15_1","volume-title":"Health data in an open world. CoRR abs\/1712.05627","author":"Culnane Chris","year":"2017","unstructured":"Chris Culnane , Benjamin I. P. Rubinstein , and Vanessa Teague . 2017. Health data in an open world. CoRR abs\/1712.05627 ( 2017 ). arXiv:1712.05627 http:\/\/arxiv.org\/abs\/1712.05627. Chris Culnane, Benjamin I. P. Rubinstein, and Vanessa Teague. 2017. Health data in an open world. CoRR abs\/1712.05627 (2017). arXiv:1712.05627 http:\/\/arxiv.org\/abs\/1712.05627."},{"key":"e_1_2_3_16_1","volume-title":"Revised Papers from the First International Workshop on Peer-to-Peer Systems (IPTPS\u201901)","author":"Douceur John R.","unstructured":"John R. Douceur . 2002. The Sybil attack . In Revised Papers from the First International Workshop on Peer-to-Peer Systems (IPTPS\u201901) . Springer-Verlag , Berlin , 251\u2013260. John R. Douceur. 2002. The Sybil attack. In Revised Papers from the First International Workshop on Peer-to-Peer Systems (IPTPS\u201901). Springer-Verlag, Berlin, 251\u2013260."},{"key":"e_1_2_3_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1985.1057074"},{"key":"e_1_2_3_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"e_1_2_3_19_1","volume-title":"Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Med. 178, 11 (11","author":"Gianfrancesco Milena A.","year":"2018","unstructured":"Milena A. Gianfrancesco , Suzanne Tamang , Jinoos Yazdany , and Gabriela Schmajuk . 2018. Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Med. 178, 11 (11 2018 ), 1544\u20131547. Milena A. Gianfrancesco, Suzanne Tamang, Jinoos Yazdany, and Gabriela Schmajuk. 2018. Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Med. 178, 11 (11 2018), 1544\u20131547."},{"key":"e_1_2_3_20_1","volume-title":"Advances in Neural Information Processing Systems 27","author":"Goodfellow Ian","unstructured":"Ian Goodfellow , Jean Pouget-Abadie , Mehdi Mirza , Bing Xu , David Warde-Farley , Sherjil Ozair , Aaron Courville , and Yoshua Bengio . 2014. Generative adversarial nets . In Advances in Neural Information Processing Systems 27 , Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.). Curran Associates, Inc. , 2672\u20132680. Retrieved from http:\/\/papers.nips.cc\/paper\/5423-generative-adversarial-nets.pdf. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 2672\u20132680. Retrieved from http:\/\/papers.nips.cc\/paper\/5423-generative-adversarial-nets.pdf."},{"key":"e_1_2_3_21_1","volume-title":"Explaining and Harnessing Adversarial Examples. arxiv:stat.ML\/1412.6572","author":"Goodfellow Ian J.","year":"2014","unstructured":"Ian J. Goodfellow , Jonathon Shlens , and Christian Szegedy . 2014. Explaining and Harnessing Adversarial Examples. arxiv:stat.ML\/1412.6572 ( 2014 ). Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and Harnessing Adversarial Examples. arxiv:stat.ML\/1412.6572 (2014)."},{"key":"e_1_2_3_22_1","doi-asserted-by":"crossref","unstructured":"S. Haykin and B. Widrow. 2003. Least-mean-Square Adaptive Filters. Wiley. 2003041161Retrieved from https:\/\/books.google.de\/books?id=U8X3mJtawUkC.  S. Haykin and B. Widrow. 2003. Least-mean-Square Adaptive Filters. Wiley. 2003041161Retrieved from https:\/\/books.google.de\/books?id=U8X3mJtawUkC.","DOI":"10.1002\/0471461288"},{"key":"e_1_2_3_23_1","volume-title":"Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. CoRR abs\/1804.00308","author":"Jagielski Matthew","year":"2018","unstructured":"Matthew Jagielski , Alina Oprea , Battista Biggio , Chang Liu , Cristina Nita-Rotaru , and Bo Li. 2018. Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. CoRR abs\/1804.00308 ( 2018 ). Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li. 2018. Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. CoRR abs\/1804.00308 (2018)."},{"key":"e_1_2_3_24_1","unstructured":"Peter Kairouz H. Brendan McMahan Brendan Avent Aur\u00e9lien Bellet Mehdi Bennis Arjun Nitin Bhagoji Keith Bonawitz Zachary Charles Graham Cormode Rachel Cummings Rafael G. L. D\u2019Oliveira Salim El Rouayheb David Evans Josh Gardner Zachary Garrett Adri\u00e0 Gasc\u00f3n Badih Ghazi Phillip B. Gibbons Marco Gruteser Zaid Harchaoui Chaoyang He Lie He Zhouyuan Huo Ben Hutchinson Justin Hsu Martin Jaggi Tara Javidi Gauri Joshi Mikhail Khodak Jakub Kone\u010dn\u00fd Aleksandra Korolova Farinaz Koushanfar Sanmi Koyejo Tancr\u00e8de Lepoint Yang Liu Prateek Mittal Mehryar Mohri Richard Nock Ayfer \u00d6zg\u00fcr Rasmus Pagh Mariana Raykova Hang Qi Daniel Ramage Ramesh Raskar Dawn Song Weikang Song Sebastian U. Stich Ziteng Sun Ananda Theertha Suresh Florian Tram\u00e8r Praneeth Vepakomma Jianyu Wang Li Xiong Zheng Xu Qiang Yang Felix X. Yu Han Yu and Sen Zhao. 2019. Advances and Open Problems in Federated Learning. arxiv:cs.LG\/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 Rafael G. L. D\u2019Oliveira Salim El Rouayheb David Evans Josh Gardner Zachary Garrett Adri\u00e0 Gasc\u00f3n Badih Ghazi Phillip B. Gibbons Marco Gruteser Zaid Harchaoui Chaoyang He Lie He Zhouyuan Huo Ben Hutchinson Justin Hsu Martin Jaggi Tara Javidi Gauri Joshi Mikhail Khodak Jakub Kone\u010dn\u00fd Aleksandra Korolova Farinaz Koushanfar Sanmi Koyejo Tancr\u00e8de Lepoint Yang Liu Prateek Mittal Mehryar Mohri Richard Nock Ayfer \u00d6zg\u00fcr Rasmus Pagh Mariana Raykova Hang Qi Daniel Ramage Ramesh Raskar Dawn Song Weikang Song Sebastian U. Stich Ziteng Sun Ananda Theertha Suresh Florian Tram\u00e8r Praneeth Vepakomma Jianyu Wang Li Xiong Zheng Xu Qiang Yang Felix X. Yu Han Yu and Sen Zhao. 2019. Advances and Open Problems in Federated Learning. arxiv:cs.LG\/1912.04977 (2019)."},{"key":"e_1_2_3_26_1","volume-title":"Deep learning. Nature 521 (05","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun , Y. Bengio , and Geoffrey Hinton . 2015. Deep learning. Nature 521 (05 2015 ), 436\u201344. DOI:DOI:https:\/\/doi.org\/10.1038\/nature14539 10.1038\/nature14539 Yann LeCun, Y. Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521 (05 2015), 436\u201344. DOI:DOI:https:\/\/doi.org\/10.1038\/nature14539"},{"key":"e_1_2_3_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2004.04.005"},{"key":"e_1_2_3_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559850"},{"key":"e_1_2_3_29_1","volume-title":"Privacy-preserving hierarchical clustering: Formal security and efficient approximation. CoRR abs\/1904.04475","author":"Meng Xianrui","year":"2019","unstructured":"Xianrui Meng , Dimitrios Papadopoulos , Alina Oprea , and Nikos Triandopoulos . 2019. Privacy-preserving hierarchical clustering: Formal security and efficient approximation. CoRR abs\/1904.04475 ( 2019 ). Xianrui Meng, Dimitrios Papadopoulos, Alina Oprea, and Nikos Triandopoulos. 2019. Privacy-preserving hierarchical clustering: Formal security and efficient approximation. CoRR abs\/1904.04475 (2019)."},{"key":"e_1_2_3_30_1","volume-title":"Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Med. 6, 7 (07","author":"Moher David","year":"2009","unstructured":"David Moher , Alessandro Liberati , Jennifer Tetzlaff , Douglas G. Altman , and The PRISMA Group . 2009. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Med. 6, 7 (07 2009 ), 1\u20136. DOI:DOI:https:\/\/doi.org\/10.1371\/journal.pmed.1000097 10.1371\/journal.pmed.1000097 David Moher, Alessandro Liberati, Jennifer Tetzlaff, Douglas G. Altman, and The PRISMA Group. 2009. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Med. 6, 7 (07 2009), 1\u20136. DOI:DOI:https:\/\/doi.org\/10.1371\/journal.pmed.1000097"},{"key":"e_1_2_3_31_1","volume-title":"Proceedings of the 37th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u201915)","author":"Monteiro E.","year":"2015","unstructured":"E. Monteiro , C. Costa , and J. L. Oliveira . 2015. A machine learning methodology for medical imaging anonymization . In Proceedings of the 37th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u201915) . 1381\u20131384. DOI:DOI:https:\/\/doi.org\/10.1109\/EMBC. 2015 .7318626 10.1109\/EMBC.2015.7318626 E. Monteiro, C. Costa, and J. L. Oliveira. 2015. A machine learning methodology for medical imaging anonymization. In Proceedings of the 37th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u201915). 1381\u20131384. DOI:DOI:https:\/\/doi.org\/10.1109\/EMBC.2015.7318626"},{"key":"e_1_2_3_32_1","volume-title":"DeepFool: A simple and accurate method to fool deep neural networks. CoRR abs\/1511.04599","author":"Moosavi-Dezfooli Seyed-Mohsen","year":"2015","unstructured":"Seyed-Mohsen Moosavi-Dezfooli , Alhussein Fawzi , and Pascal Frossard . 2015. DeepFool: A simple and accurate method to fool deep neural networks. CoRR abs\/1511.04599 ( 2015 ). arXiv:1511.04599 http:\/\/arxiv.org\/abs\/1511.04599. Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2015. DeepFool: A simple and accurate method to fool deep neural networks. CoRR abs\/1511.04599 (2015). arXiv:1511.04599 http:\/\/arxiv.org\/abs\/1511.04599."},{"key":"e_1_2_3_33_1","volume-title":"Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464","author":"Obermeyer Ziad","year":"2019","unstructured":"Ziad Obermeyer , Brian Powers , Christine Vogeli , and Sendhil Mullainathan . 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 ( 2019 ), 447\u2013453. Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 (2019), 447\u2013453."},{"key":"e_1_2_3_34_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756123.1756146"},{"key":"e_1_2_3_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-012-0035-5"},{"key":"e_1_2_3_36_1","volume-title":"Proceedings of the IEEE 14th International Conference on Machine Learning and Applications (ICMLA\u201915)","author":"Ribeiro Mauro","unstructured":"Mauro Ribeiro , Katarina Grolinger , and Miriam A. M. Capretz . 2015. MLaaS: Machine learning as a service . In Proceedings of the IEEE 14th International Conference on Machine Learning and Applications (ICMLA\u201915) . IEEE, 896\u2013902. Mauro Ribeiro, Katarina Grolinger, and Miriam A. M. Capretz. 2015. MLaaS: Machine learning as a service. In Proceedings of the IEEE 14th International Conference on Machine Learning and Applications (ICMLA\u201915). IEEE, 896\u2013902."},{"key":"e_1_2_3_37_1","unstructured":"R. L. Rivest L. Adleman and M. L. Dertouzos. 1978. On data banks and privacy homomorphisms. Foundations of Secure Computation. Academia Press 169\u2013179.  R. L. Rivest L. Adleman and M. L. Dertouzos. 1978. On data banks and privacy homomorphisms. Foundations of Secure Computation. Academia Press 169\u2013179."},{"key":"e_1_2_3_38_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-10933-3"},{"key":"e_1_2_3_39_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1026543900054"},{"key":"e_1_2_3_40_1","volume-title":"Proceedings of the IEEE 38th International Conference on Distributed Computing Systems (ICDCS\u201918)","author":"Shae Z.","year":"2018","unstructured":"Z. Shae and J. Tsai . 2018. Transform blockchain into distributed parallel computing architecture for precision medicine . In Proceedings of the IEEE 38th International Conference on Distributed Computing Systems (ICDCS\u201918) . 1290\u20131299. DOI:DOI:https:\/\/doi.org\/10.1109\/ICDCS. 2018 .00129 10.1109\/ICDCS.2018.00129 Z. Shae and J. Tsai. 2018. Transform blockchain into distributed parallel computing architecture for precision medicine. In Proceedings of the IEEE 38th International Conference on Distributed Computing Systems (ICDCS\u201918). 1290\u20131299. DOI:DOI:https:\/\/doi.org\/10.1109\/ICDCS.2018.00129"},{"key":"e_1_2_3_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/359168.359176"},{"key":"e_1_2_3_42_1","first-page":"7","article-title":"PPDM: A privacy-preserving protocol for cloud-assisted e-Healthcare systems","volume":"9","author":"Zhou J.","year":"2015","unstructured":"J. Zhou , Z. Cao , X. Dong , and X. Lin . 2015 . PPDM: A privacy-preserving protocol for cloud-assisted e-Healthcare systems . IEEE J. Select. Top. Sig. Proc. 9 , 7 (Oct. 2015), 1332\u20131344. DOI:DOI:https:\/\/doi.org\/10.1109\/JSTSP.2015.2427113 10.1109\/JSTSP.2015.2427113 J. Zhou, Z. Cao, X. Dong, and X. Lin. 2015. PPDM: A privacy-preserving protocol for cloud-assisted e-Healthcare systems. IEEE J. Select. Top. Sig. Proc. 9, 7 (Oct. 2015), 1332\u20131344. DOI:DOI:https:\/\/doi.org\/10.1109\/JSTSP.2015.2427113","journal-title":"IEEE J. Select. Top. Sig. Proc."}],"container-title":["ACM Transactions on Internet Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3412357","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3412357","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:25:01Z","timestamp":1750195501000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3412357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,2]]},"references-count":123,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,6,23]]}},"alternative-id":["10.1145\/3412357"],"URL":"https:\/\/doi.org\/10.1145\/3412357","relation":{},"ISSN":["1533-5399","1557-6051"],"issn-type":[{"value":"1533-5399","type":"print"},{"value":"1557-6051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,2]]},"assertion":[{"value":"2020-02-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}