{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:48:22Z","timestamp":1781016502504,"version":"3.54.1"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB2102100"],"award-info":[{"award-number":["2019YFB2102100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61603376"],"award-info":[{"award-number":["61603376"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9534451","type":"proceedings-article","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T16:40:52Z","timestamp":1632242452000},"page":"1-8","source":"Crossref","is-referenced-by-count":23,"title":["FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning"],"prefix":"10.1109","author":[{"given":"Bingjie","family":"Yan","sequence":"first","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Science,Shenzhen,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyi","family":"Liu","sequence":"additional","affiliation":[{"name":"SKL-IoTSC University of Macau,Macau SAR,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lujia","family":"Wang","sequence":"additional","affiliation":[{"name":"Cloud Computing Center SIAT, CAS,Shenzhen,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yize","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Science Hainan University,Haikou,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixuan","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Computing PolyU,HongKong,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[{"name":"RAM-LAB HKUST HongKong,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng-Zhong","family":"Xu","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, University of Macau,Macau SAR,China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","author":"hardy","year":"2017","journal-title":"ar Xiv preprint"},{"key":"ref11","doi-asserted-by":"crossref","DOI":"10.1109\/MIS.2020.2988604","article-title":"Fedhealth: A federated transfer learning framework for wearable healthcare","author":"chen","year":"2020","journal-title":"IEEE Intelligent Systems"},{"key":"ref12","first-page":"1333","article-title":"Privacy-preserving deep learning via additively homomorphic encryption","volume":"13","author":"aono","year":"2017","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2942190"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2019.2921755"},{"key":"ref15","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?ny","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2919736"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/BIGCOM.2019.00030"},{"key":"ref18","article-title":"Differentially private federated learning: A client level perspective","author":"geyer","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref19","article-title":"Elfish: Resource-aware federated learning on heterogeneous edge devices","author":"xu","year":"2019","journal-title":"ar Xiv preprint"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2967772"},{"key":"ref6","first-page":"58","article-title":"Optimization in federated learning","author":"felbab","year":"2019","journal-title":"ITAT"},{"key":"ref5","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"brendan mcmahan","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852464"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006179"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2984511.2984540"},{"key":"ref9","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2940820"},{"key":"ref22","author":"cong","year":"2020","journal-title":"Optimal procurement auction for cooperative production of virtual products Vickrey-clarke-groves meet cremer-mclean"},{"key":"ref21","article-title":"How to backdoor federated learning. corr","author":"bagdasaryan","year":"2018","journal-title":"ArXiv Preprint"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","location":"Shenzhen, China","start":{"date-parts":[[2021,7,18]]},"end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09534451.pdf?arnumber=9534451","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T19:51:57Z","timestamp":1778183517000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9534451\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9534451","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}