{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T13:25:10Z","timestamp":1773926710058,"version":"3.50.1"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100018531","name":"Major Science and Technology Projects in Yunnan Province","doi-asserted-by":"publisher","award":["202302AG050009"],"award-info":[{"award-number":["202302AG050009"]}],"id":[{"id":"10.13039\/501100018531","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62471205"],"award-info":[{"award-number":["62471205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.eswa.2025.130570","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T16:42:43Z","timestamp":1764088963000},"page":"130570","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DAG-Driven Optimization for heterogeneous federated learning based on fuzzy entropy and benders decomposition"],"prefix":"10.1016","volume":"302","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2505-0288","authenticated-orcid":false,"given":"Fenhua","family":"Bai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0736-3437","authenticated-orcid":false,"given":"Chunlin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1273-7950","authenticated-orcid":false,"given":"Tao","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2662-1596","authenticated-orcid":false,"given":"Kai","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5057-6372","authenticated-orcid":false,"given":"Xiaohui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4218-105X","authenticated-orcid":false,"given":"Chengjiang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.eswa.2025.130570_bib0001","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/MCOM.001.2300419","article-title":"Mitigating the communication straggler effect in federated learning via named data networking","volume":"62","author":"Amadeo","year":"2024","journal-title":"IEEE Communications Magazine"},{"key":"10.1016\/j.eswa.2025.130570_bib0002","series-title":"Federated learning with manifold regularization and normalized update reaggregation","volume":"vol. 36","author":"An","year":"2023"},{"key":"10.1016\/j.eswa.2025.130570_bib0003","article-title":"Agent architecture of an intelligent medical system based on federated learning and blockchain technology","volume":"58","author":"ap","year":"2021","journal-title":"Journal of Information Security and Applications"},{"key":"10.1016\/j.eswa.2025.130570_bib0004","series-title":"International conference on artificial intelligence and statistics","first-page":"2938","author":"Bagdasaryan","year":"2020"},{"issue":"12","key":"10.1016\/j.eswa.2025.130570_bib0005","first-page":"1","article-title":"When federated learning meets privacy-preserving computation","volume":"56","author":"Chen","year":"2024","journal-title":"ACM Computing Surveys"},{"issue":"12","key":"10.1016\/j.eswa.2025.130570_bib0006","doi-asserted-by":"crossref","first-page":"9367","DOI":"10.1109\/JIOT.2021.3110412","article-title":"Multitask offloading strategy optimization based on directed acyclic graphs for edge computing","volume":"9","author":"Chen","year":"2021","journal-title":"IEEE Internet of Things Journal"},{"issue":"12","key":"10.1016\/j.eswa.2025.130570_bib0007","doi-asserted-by":"crossref","first-page":"11604","DOI":"10.1109\/TMC.2024.3396511","article-title":"Joint computation offloading and resource allocation in multi-edge smart communities with personalized federated deep reinforcement learning","volume":"23","author":"Chen","year":"2024","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"10.1016\/j.eswa.2025.130570_bib0008","series-title":"Proceedings of The 25th International Conference on Artificial Intelligence and Statistics","first-page":"10351","article-title":"Towards understanding biased client selection in federated learning","volume":"151","author":"Cho","year":"2022"},{"issue":"4","key":"10.1016\/j.eswa.2025.130570_bib0009","doi-asserted-by":"crossref","first-page":"1910","DOI":"10.1109\/TCCN.2022.3196009","article-title":"Federated learning over wireless channels: Dynamic resource allocation and task scheduling","volume":"8","author":"Chu","year":"2022","journal-title":"IEEE Transactions on Cognitive Communications and Networking"},{"issue":"2","key":"10.1016\/j.eswa.2025.130570_bib0010","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1109\/TR.2024.3474710","article-title":"Bfkd: Blockchain-based federated knowledge distillation for aviation internet of things","volume":"74","author":"Deng","year":"2025","journal-title":"IEEE Transactions on Reliability"},{"key":"10.1016\/j.eswa.2025.130570_bib0011","series-title":"2024 IEEE international conference on big data (bigdata)","first-page":"7725","article-title":"A survey on model-heterogeneous federated learning: problems, methods, and prospects","author":"Fan","year":"2024"},{"issue":"5","key":"10.1016\/j.eswa.2025.130570_bib0012","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/TC.2021.3072033","article-title":"Bafl: A blockchain-based asynchronous federated learning framework","volume":"71","author":"Feng","year":"2021","journal-title":"IEEE Transactions on Computers"},{"key":"10.1016\/j.eswa.2025.130570_bib0013","first-page":"13448","author":"Goebel","year":"2023"},{"key":"10.1016\/j.eswa.2025.130570_bib0014","series-title":"Congress on blockchain and applications","first-page":"186","article-title":"Blockchain-based federated learning: Incentivizing data sharing and penalizing dishonest behavior","author":"Jaberzadeh","year":"2023"},{"issue":"1\u20132","key":"10.1016\/j.eswa.2025.130570_bib0015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"key":"10.1016\/j.eswa.2025.130570_bib0016","series-title":"International conference on machine learning","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","author":"Karimireddy","year":"2020"},{"issue":"14","key":"10.1016\/j.eswa.2025.130570_bib0017","doi-asserted-by":"crossref","first-page":"16301","DOI":"10.1109\/JSEN.2021.3076767","article-title":"Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging","volume":"21","author":"Kumar","year":"2021","journal-title":"IEEE Sensors Journal"},{"key":"10.1016\/j.eswa.2025.130570_bib0018","series-title":"15th usenix symposium on operating systems design and implementation (osdi 21), usenix association","first-page":"19","article-title":"Oort: Efficient federated learning via guided participant selection","author":"Lai","year":"2021"},{"key":"10.1016\/j.eswa.2025.130570_bib0019","series-title":"Icc 2023 - IEEE international conference on communications","first-page":"1976","article-title":"Distributed learning in heterogeneous environment: federated learning with adaptive aggregation and computation reduction","author":"Li","year":"2023"},{"key":"10.1016\/j.eswa.2025.130570_bib0020","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"10.1016\/j.eswa.2025.130570_bib0021","unstructured":"Li, X., Jiang, M., Zhang, X., Kamp, M., & Dou, Q. (2021). Fedbn: Federated learning on non-iid features via local batch normalization. Technical Report arXiv preprint arXiv: 2102.07623."},{"issue":"3","key":"10.1016\/j.eswa.2025.130570_bib0022","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","article-title":"Federated learning in mobile edge networks: A comprehensive survey","volume":"22","author":"Lim","year":"2020","journal-title":"IEEE communications Surveys & Tutorials"},{"key":"10.1016\/j.eswa.2025.130570_bib0023","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"13900","article-title":"Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update","volume":"38","author":"Liu","year":"2024"},{"issue":"6","key":"10.1016\/j.eswa.2025.130570_bib0024","doi-asserted-by":"crossref","first-page":"4177","DOI":"10.1109\/TII.2019.2942190","article-title":"Blockchain and federated learning for privacy-preserved data sharing in industrial iot","volume":"16","author":"Lu","year":"2020","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"3","key":"10.1016\/j.eswa.2025.130570_bib0025","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MCI.2022.3180932","article-title":"When federated learning meets blockchain: A new distributed learning paradigm","volume":"17","author":"Ma","year":"2022","journal-title":"IEEE Computational Intelligence Magazine"},{"key":"10.1016\/j.eswa.2025.130570_bib0026","series-title":"Communication-efficient learning of deep networks from decentralized data","first-page":"1273","author":"Mcmahan","year":"2017"},{"key":"10.1016\/j.eswa.2025.130570_bib0027","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.future.2020.10.007","article-title":"A survey on security and privacy of federated learning","volume":"115","author":"Mothukuri","year":"2021","journal-title":"Future Generation Computer Systems"},{"key":"10.1016\/j.eswa.2025.130570_bib0028","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.future.2020.10.007","article-title":"A survey on security and privacy of federated learning","volume":"115","author":"Mothukuri","year":"2021","journal-title":"Future Generation Computer Systems"},{"key":"10.1016\/j.eswa.2025.130570_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108128","article-title":"A comprehensive review on federated learning for data-sensitive application: Open issues & challenges","volume":"133","author":"Narula","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"3","key":"10.1016\/j.eswa.2025.130570_bib0030","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1109\/COMST.2021.3075439","article-title":"Federated learning for internet of things: A comprehensive survey","volume":"23","author":"Nguyen","year":"2021","journal-title":"IEEE Communications Surveys & Tutorials"},{"issue":"16","key":"10.1016\/j.eswa.2025.130570_bib0031","doi-asserted-by":"crossref","first-page":"12806","DOI":"10.1109\/JIOT.2021.3072611","article-title":"Federated learning meets blockchain in edge computing: Opportunities and challenges","volume":"8","author":"Nguyen","year":"2021","journal-title":"IEEE Internet of Things Journal"},{"issue":"1","key":"10.1016\/j.eswa.2025.130570_bib0032","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/JSAC.2020.3036952","article-title":"Fast-convergent federated learning","volume":"39","author":"Nguyen","year":"2020","journal-title":"IEEE Journal on Selected Areas in Communications"},{"issue":"4","key":"10.1016\/j.eswa.2025.130570_bib0033","doi-asserted-by":"crossref","first-page":"4290","DOI":"10.1109\/TDSC.2023.3326230","article-title":"Perfectly accurate membership inference by a dishonest central server in federated learning","volume":"21","author":"Pichler","year":"2023","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"issue":"4","key":"10.1016\/j.eswa.2025.130570_bib0034","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3524104","article-title":"Blockchain-enabled federated learning: A survey","volume":"55","author":"Qu","year":"2022","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.eswa.2025.130570_bib0035","series-title":"2024 IEEE 100th vehicular technology conference (vtc2024-fall)","first-page":"1","article-title":"Blockfl: A blockchain-enabled federated learning system for securing iovs","author":"Rahman","year":"2024"},{"key":"10.1016\/j.eswa.2025.130570_bib0036","series-title":"Proceedings of the 20th annual international conference on mobile systems, applications and services","first-page":"436","article-title":"Fedbalancer: Data and pace control for efficient federated learning on heterogeneous clients","author":"Shin","year":"2022"},{"issue":"4","key":"10.1016\/j.eswa.2025.130570_bib0037","doi-asserted-by":"crossref","first-page":"4289","DOI":"10.1109\/TPAMI.2022.3196503","article-title":"Decentralized federated averaging","volume":"45","author":"Sun","year":"2023","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"10.1016\/j.eswa.2025.130570_bib0038","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MC.2021.3052390","article-title":"Federated learning: The pioneering distributed machine learning and privacy-preserving data technology","volume":"55","author":"Treleaven","year":"2022","journal-title":"Computer"},{"key":"10.1016\/j.eswa.2025.130570_bib0039","series-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume":"vol. 33","author":"Wang","year":"2020"},{"issue":"6","key":"10.1016\/j.eswa.2025.130570_bib0040","doi-asserted-by":"crossref","first-page":"918","DOI":"10.26599\/TST.2021.9010029","article-title":"Dataflow management in the internet of things: sensing, control, and security","volume":"26","author":"Wei","year":"2021","journal-title":"Tsinghua Science and Technology"},{"key":"10.1016\/j.eswa.2025.130570_bib0041","series-title":"A review of blockchain platforms based on the scalability, security and decentralization trilemma","first-page":"146","author":"Werth","year":"2023"},{"issue":"4","key":"10.1016\/j.eswa.2025.130570_bib0042","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1109\/TSC.2023.3336980","article-title":"Decentralized and incentivized federated learning: A blockchain-enabled framework utilising compressed soft-labels and peer consistency","volume":"17","author":"Witt","year":"2023","journal-title":"IEEE Transactions on Services Computing"},{"key":"10.1016\/j.eswa.2025.130570_bib0043","article-title":"Learning to invert: Simple adaptive attacks for gradient inversion in federated learning","volume":"vol. 216","author":"Wu","year":"2023"},{"key":"10.1016\/j.eswa.2025.130570_bib0044","series-title":"Flare: A new federated learning framework with adjustable learning rates over resource-constrained wireless networks","author":"Xiao","year":"2025"},{"issue":"12","key":"10.1016\/j.eswa.2025.130570_bib0045","doi-asserted-by":"crossref","first-page":"8751","DOI":"10.1109\/TWC.2023.3265458","article-title":"Joint optimization of security strength and resource allocation for computation offloading in vehicular edge computing","volume":"22","author":"Xiao","year":"2023","journal-title":"IEEE Transactions on Wireless Communications"},{"issue":"9","key":"10.1016\/j.eswa.2025.130570_bib0046","first-page":"1781","article-title":"A survey of attack and defense techniques for federated learning systems","volume":"46","author":"Xiaofeng","year":"2023","journal-title":"Chinese Journal of Computers"},{"key":"10.1016\/j.eswa.2025.130570_bib0047","unstructured":"Xie, C., Koyejo, S., & Gupta, I. (2019). Asynchronous federated optimization. Technical Report arXiv: 1903.03934\">."},{"key":"10.1016\/j.eswa.2025.130570_bib0048","series-title":"Federated model heterogeneous matryoshka representation learning","volume":"vol. 37","author":"Yi","year":"2024"},{"key":"10.1016\/j.eswa.2025.130570_bib0049","series-title":"2020 IEEE international conference on communications workshops (icc workshops)","first-page":"1","article-title":"Energy-efficient radio resource allocation for federated edge learning","author":"Zeng","year":"2020"},{"issue":"3","key":"10.1016\/j.eswa.2025.130570_bib0050","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1109\/JIOT.2020.3017377","article-title":"Privacy-preserving blockchain-based federated learning for iot devices","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Internet of Things Journal"},{"issue":"2","key":"10.1016\/j.eswa.2025.130570_bib0051","doi-asserted-by":"crossref","DOI":"10.1109\/JIOT.2021.3091142","article-title":"Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing","volume":"9","author":"Zhou","year":"2022","journal-title":"IEEE Internet of Things Journal"},{"issue":"4","key":"10.1016\/j.eswa.2025.130570_bib0052","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/IOTM.001.2300099","article-title":"Toward scalable wireless federated learning: challenges and solutions","volume":"6","author":"Zhou","year":"2023","journal-title":"IEEE Internet of Things Magazine"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417425041855?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417425041855?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T09:54:44Z","timestamp":1773914084000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417425041855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":52,"alternative-id":["S0957417425041855"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2025.130570","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DAG-Driven Optimization for heterogeneous federated learning based on fuzzy entropy and benders decomposition","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2025.130570","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"130570"}}