{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T19:32:01Z","timestamp":1781206321249,"version":"3.54.1"},"reference-count":32,"publisher":"Elsevier BV","issue":"2","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T00:00:00Z","timestamp":1735257600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB2703100"],"award-info":[{"award-number":["2022YFB2703100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Blockchain: Research and Applications"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1016\/j.bcra.2024.100271","type":"journal-article","created":{"date-parts":[[2025,1,5]],"date-time":"2025-01-05T06:38:35Z","timestamp":1736059115000},"page":"100271","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"title":["\u03c0FL: Private, atomic, incentive mechanism for federated learning based on blockchain"],"prefix":"10.1016","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3779-3045","authenticated-orcid":false,"given":"Kejia","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuanming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zunlei","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bcra.2024.100271_br0100","doi-asserted-by":"crossref","first-page":"10700","DOI":"10.1109\/JIOT.2019.2940820","article-title":"Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory","volume":"6","author":"Kang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.bcra.2024.100271_br0150","series-title":"Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data","first-page":"1187","article-title":"Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation","author":"Li","year":"2014"},{"key":"10.1016\/j.bcra.2024.100271_br0220","series-title":"Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security","first-page":"1310","article-title":"Privacy-preserving deep learning","author":"Shokri","year":"2015"},{"key":"10.1016\/j.bcra.2024.100271_br0170","author":"Li"},{"issue":"6","key":"10.1016\/j.bcra.2024.100271_br0260","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/JSAC.2019.2904348","article-title":"Adaptive federated learning in resource constrained edge computing systems","volume":"37","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"10","key":"10.1016\/j.bcra.2024.100271_br0210","doi-asserted-by":"crossref","first-page":"8456","DOI":"10.1109\/JIOT.2020.3046509","article-title":"Fogfl: fog-assisted federated learning for resource-constrained IoT devices","volume":"8","author":"Saha","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.bcra.2024.100271_br0320","series-title":"Proceedings of the 2021 IEEE Wireless Communications and Networking Conference (WCNC)","first-page":"1","article-title":"Reputation-based regional federated learning for knowledge trading in blockchain-enhanced IoV","author":"Zou","year":"2021"},{"key":"10.1016\/j.bcra.2024.100271_br0190","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"issue":"3","key":"10.1016\/j.bcra.2024.100271_br0160","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","article-title":"Federated learning: challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"issue":"2","key":"10.1016\/j.bcra.2024.100271_br0250","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/TPDS.2020.3023905","article-title":"Towards efficient scheduling of federated mobile devices under computational and statistical heterogeneity","volume":"32","author":"Wang","year":"2021","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.bcra.2024.100271_br0070","series-title":"Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics","first-page":"1167","article-title":"Towards efficient data valuation based on the Shapley value","author":"Jia","year":"2019"},{"key":"10.1016\/j.bcra.2024.100271_br0060","series-title":"Proceedings of International Conference on Machine Learning","first-page":"2242","article-title":"Data Shapley: equitable valuation of data for machine learning","author":"Ghorbani","year":"2019"},{"key":"10.1016\/j.bcra.2024.100271_br0080","series-title":"Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"8235","article-title":"Scalability vs. utility: do we have to sacrifice one for the other in data importance quantification?","author":"Jia","year":"2021"},{"key":"10.1016\/j.bcra.2024.100271_br0120","author":"Kwon"},{"key":"10.1016\/j.bcra.2024.100271_br0090","author":"Jiang"},{"key":"10.1016\/j.bcra.2024.100271_br0030","series-title":"European Symposium on Research in Computer Security","first-page":"497","article-title":"Flod: oblivious defender for private byzantine-robust federated learning with dishonest-majority","author":"Dong","year":"2021"},{"key":"10.1016\/j.bcra.2024.100271_br0040","series-title":"Theory of Cryptography: Third Theory of Cryptography Conference","first-page":"265","article-title":"Calibrating noise to sensitivity in private data analysis","author":"Dwork","year":"2006"},{"key":"10.1016\/j.bcra.2024.100271_br0010","series-title":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security","first-page":"1175","article-title":"Practical secure aggregation for privacy-preserving machine learning","author":"Bonawitz","year":"2017"},{"key":"10.1016\/j.bcra.2024.100271_br0240","series-title":"Proceedings of the 2019 IEEE International Conference on Big Data (Big Data)","first-page":"395","article-title":"Mechanism design for an incentive-aware blockchain-enabled federated learning platform","author":"Toyoda","year":"2019"},{"key":"10.1016\/j.bcra.2024.100271_br0050","author":"Fallah"},{"key":"10.1016\/j.bcra.2024.100271_br0180","series-title":"Proceedings of Asian Conference on Machine Learning","first-page":"1253","article-title":"Pfedatt: attention-based personalized federated learning on heterogeneous clients","author":"Ma","year":"2021"},{"issue":"8","key":"10.1016\/j.bcra.2024.100271_br0130","doi-asserted-by":"crossref","first-page":"4874","DOI":"10.1109\/TWC.2021.3062708","article-title":"An incentive mechanism for federated learning in wireless cellular networks: an auction approach","volume":"20","author":"Le","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"10.1016\/j.bcra.2024.100271_br0300","series-title":"Proceedings of the 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","first-page":"278","article-title":"Fmore: an incentive scheme of multi-dimensional auction for federated learning in mec","author":"Zeng","year":"2020"},{"issue":"12","key":"10.1016\/j.bcra.2024.100271_br0270","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1109\/JSAC.2022.3213323","article-title":"InFEDge: a blockchain-based incentive mechanism in hierarchical federated learning for end-edge-cloud communications","volume":"40","author":"Wang","year":"2022","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"12","key":"10.1016\/j.bcra.2024.100271_br0230","doi-asserted-by":"crossref","first-page":"3805","DOI":"10.1109\/JSAC.2021.3118354","article-title":"Pain-fl: personalized privacy-preserving incentive for federated learning","volume":"39","author":"Sun","year":"2021","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"10.1016\/j.bcra.2024.100271_br0290","article-title":"Deep sets","volume":"30","author":"Zaheer","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bcra.2024.100271_br0200","first-page":"7057","article-title":"Fedsplit: an algorithmic framework for fast federated optimization","volume":"33","author":"Pathak","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bcra.2024.100271_br0020","series-title":"Advances in Cryptology \u2013 CRYPTO 2012","first-page":"643","article-title":"Multiparty computation from somewhat homomorphic encryption","author":"Damg\u00e5rd","year":"2012"},{"key":"10.1016\/j.bcra.2024.100271_br0140","author":"LeCun"},{"key":"10.1016\/j.bcra.2024.100271_br0280","author":"Xiao"},{"key":"10.1016\/j.bcra.2024.100271_br0310","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"5150","article-title":"Joint geometrical and statistical alignment for visual domain adaptation","author":"Zhang","year":"2017"},{"key":"10.1016\/j.bcra.2024.100271_br0110","series-title":"International Conference on Machine Learning","first-page":"1885","article-title":"Understanding black-box predictions via influence functions","author":"Koh","year":"2017"}],"container-title":["Blockchain: Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2096720924000848?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2096720924000848?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T19:13:14Z","timestamp":1750965194000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2096720924000848"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":32,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["S2096720924000848"],"URL":"https:\/\/doi.org\/10.1016\/j.bcra.2024.100271","relation":{},"ISSN":["2096-7209"],"issn-type":[{"value":"2096-7209","type":"print"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"\u03c0FL: Private, atomic, incentive mechanism for federated learning based on blockchain","name":"articletitle","label":"Article Title"},{"value":"Blockchain: Research and Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bcra.2024.100271","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 THE AUTHORS. Published by Elsevier B.V. on behalf of Zhejiang University Press.","name":"copyright","label":"Copyright"}],"article-number":"100271"}}