{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T05:25:50Z","timestamp":1778304350305,"version":"3.51.4"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Blockchain@UBC"},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2019-06348"],"award-info":[{"award-number":["RGPIN-2019-06348"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2020-05410"],"award-info":[{"award-number":["RGPIN-2020-05410"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2021-02970"],"award-info":[{"award-number":["RGPIN-2021-02970"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["DGECR-2021-00187"],"award-info":[{"award-number":["DGECR-2021-00187"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014613","name":"Public Safety Canada","doi-asserted-by":"publisher","award":["NS-5001-22170"],"award-info":[{"award-number":["NS-5001-22170"]}],"id":[{"id":"10.13039\/100014613","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Pearl River Talent Recruitment","award":["2019ZT08X603"],"award-info":[{"award-number":["2019ZT08X603"]}]},{"name":"Guangdong Pearl River Talent Plan","award":["2019JC01X235"],"award-info":[{"award-number":["2019JC01X235"]}]},{"DOI":"10.13039\/501100010877","name":"Shenzhen Science and Technology Innovation Commission","doi-asserted-by":"publisher","award":["R2020A045"],"award-info":[{"award-number":["R2020A045"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UBC PMC-Sierra"},{"DOI":"10.13039\/100013196","name":"Western Canada Research Grid","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013196","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013020","name":"Compute Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013020","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1109\/tmc.2023.3325334","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T18:27:49Z","timestamp":1697740069000},"page":"6712-6730","source":"Crossref","is-referenced-by-count":4,"title":["Accelerating and Securing Blockchain-Enabled Distributed Machine Learning"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9086-5467","authenticated-orcid":false,"given":"Yao","family":"Du","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9040-847X","authenticated-orcid":false,"given":"Zehua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9911-2069","authenticated-orcid":false,"given":"Cyril","family":"Leung","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3529-2640","authenticated-orcid":false,"given":"Victor C. M.","family":"Leung","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IWQoS54832.2022.9812927"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3126076"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3063686"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03583-3"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833647"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3072611"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3017377"},{"key":"ref10","first-page":"1","article-title":"On the convergence of FedAvg on non-IID data","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Li"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref12","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. 31st Conf. Neural Inf. Process. Syst.","author":"Blanchard"},{"key":"ref13","first-page":"1605","article-title":"Local model poisoning attacks to Byzantine-Robust federated learning","volume-title":"Proc. 29th USENIX Secur. Symp.","author":"Fang"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24498"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2975911"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3213341"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/tmc.2023.3303017"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118352"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2942190"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3098022"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2020.3014385"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3056773"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.021.1900617"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref25","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. 33rd Conf. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2986803"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2019.2921755"},{"key":"ref28","first-page":"1387","article-title":"Energy efficiency of mobile clients in cloud computing","volume-title":"Proc. 2nd USENIX Workshop Hot Top. Cloud Comput.","author":"Miettinen"},{"key":"ref29","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(05)80010-3"},{"key":"ref31","first-page":"1","article-title":"Split learning for health: Distributed deep learning without sharing raw patient data","volume-title":"Proc. 7th Int. Conf. Learn. Reps. Workshop AI Soc. Good","author":"Vepakomma"},{"key":"ref32","first-page":"301","article-title":"The limitations of federated learning in sybil settings","volume-title":"Proc. 23rd Int. Symp. Res. Attacks, Intrusions Defenses","author":"Fung"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88418-5_22"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3169918"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3184400"},{"key":"ref36","volume-title":"The Differencing Method of Set Partitioning","author":"Karmarkar","year":"1982"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622396"},{"key":"ref38","first-page":"4427","article-title":"Federated multi-task learning","volume-title":"Proc. 31st Conf. Neural Inf. Process. Syst.","author":"Smith"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/BF01014884"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-96884-1_26"},{"key":"ref41","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019"},{"key":"ref42","first-page":"21 394","article-title":"Personalized federated learning with Moreau envelopes","volume-title":"Proc. 34th Conf. Neural Inf. Process. Syst.","author":"Dinh"},{"key":"ref43","first-page":"437","article-title":"A public domain dataset for human activity recognition using smartphones","volume-title":"Proc. 21st Int. Eur. Symp. Artif. Neural Netw., Comput. Intell. Mach. Learn.","author":"Anguita"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref45","first-page":"8410","article-title":"Scaling provable adversarial defenses","volume-title":"Proc. 32nd Conf. Neural Inf. Process. Syst.","author":"Wong"},{"issue":"2014","key":"ref46","first-page":"1","article-title":"Ethereum: A secure decentralised generalised transaction ledger","volume":"151","author":"Wood","year":"2014","journal-title":"Ethereum Project Yellow Paper"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/NTMS.2018.8328742"},{"key":"ref48","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Yin"},{"key":"ref49","first-page":"7252","article-title":"Bayesian nonparametric federated learning of neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Yurochkin"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ATNAC.2017.8215367"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7755\/10521901\/10288252.pdf?arnumber=10288252","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T18:05:25Z","timestamp":1725905125000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10288252\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":50,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2023.3325334","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6]]}}}