{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:13:50Z","timestamp":1783037630917,"version":"3.54.6"},"reference-count":46,"publisher":"Elsevier BV","issue":"1","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":[[2025,7,6]],"date-time":"2025-07-06T00:00:00Z","timestamp":1751760000000},"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","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2024YFE0200500"],"award-info":[{"award-number":["2024YFE0200500"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["High-Confidence Computing"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.hcc.2025.100343","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T11:23:15Z","timestamp":1751973795000},"page":"100343","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"title":["CHPFL: Clustered adaptive hierarchical federated learning for edge-level personalization"],"prefix":"10.1016","volume":"6","author":[{"given":"Lihua","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6014-0396","authenticated-orcid":false,"given":"Honglu","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhua","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.hcc.2025.100343_b1","series-title":"2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE)","first-page":"1","article-title":"Artificial intelligence, machine learning and deep learning","author":"Ongsulee","year":"2017"},{"issue":"7","key":"10.1016\/j.hcc.2025.100343_b2","doi-asserted-by":"crossref","first-page":"5476","DOI":"10.1109\/JIOT.2020.3030072","article-title":"A survey on federated learning: The journey from centralized to distributed on-site learning and beyond","volume":"8","author":"Abdulrahman","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.hcc.2025.100343_b3","article-title":"Federated learning: Collaborative machine learning without centralized training data","volume":"3","author":"McMahan","year":"2017","journal-title":"Google Res. Blog"},{"issue":"99","key":"10.1016\/j.hcc.2025.100343_b4","first-page":"1","article-title":"Privacy threat and defense for federated learning with non-i.i.d. Data in aIoT","volume":"PP","author":"Xiong","year":"2021","journal-title":"IEEE Trans. Ind. Informatics"},{"issue":"6","key":"10.1016\/j.hcc.2025.100343_b5","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1109\/TPDS.2021.3112604","article-title":"A potential game theoretic approach to computation offloading strategy optimization in end-edge-cloud computing","volume":"33","author":"Ding","year":"2021","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"2","key":"10.1016\/j.hcc.2025.100343_b6","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1109\/TCCN.2020.3018159","article-title":"Collaborative cloud-edge-end task offloading in mobile-edge computing networks with limited communication capability","volume":"7","author":"Kai","year":"2020","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"10.1016\/j.hcc.2025.100343_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.hcc.2021.100008","article-title":"A survey of federated learning for edge computing: Research problems and solutions","author":"Xia","year":"2021","journal-title":"High- Confid. Comput."},{"key":"10.1016\/j.hcc.2025.100343_b8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/OJCS.2020.2993259","article-title":"Personalized federated learning for intelligent IoT applications: A cloud-edge based framework","volume":"1","author":"Wu","year":"2020","journal-title":"IEEE Open J. Comput. Soc."},{"key":"10.1016\/j.hcc.2025.100343_b9","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"10537","article-title":"Federated generative model on multi-source heterogeneous data in iot","volume":"37","author":"Xiong","year":"2023"},{"issue":"5","key":"10.1016\/j.hcc.2025.100343_b10","doi-asserted-by":"crossref","first-page":"3088","DOI":"10.1109\/JIOT.2020.3007662","article-title":"Realizing the heterogeneity: A self-organized federated learning framework for IoT","volume":"8","author":"Pang","year":"2021","journal-title":"IEEE Internet Things J."},{"issue":"1\u20132","key":"10.1016\/j.hcc.2025.100343_b11","first-page":"1","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends\u00ae Mach. Learn."},{"issue":"2","key":"10.1016\/j.hcc.2025.100343_b12","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TII.2021.3073925","article-title":"Privacy threat and defense for federated learning with non-iid data in aIoT","volume":"18","author":"Xiong","year":"2021","journal-title":"IEEE Trans. Ind. Informatics"},{"key":"10.1016\/j.hcc.2025.100343_b13","series-title":"Fedmd: Heterogenous federated learning via model distillation","author":"Li","year":"2019"},{"key":"10.1016\/j.hcc.2025.100343_b14","series-title":"Adaptive personalized federated learning","author":"Deng","year":"2020"},{"key":"10.1016\/j.hcc.2025.100343_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.hcc.2025.100322","article-title":"FedBS: Solving data heterogeneity issue in federated learning using balanced subtasks","author":"Su","year":"2025","journal-title":"High- Confid. Comput."},{"key":"10.1016\/j.hcc.2025.100343_b16","article-title":"Federated multi-task learning","volume":"30","author":"Smith","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.hcc.2025.100343_b17","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"10.1016\/j.hcc.2025.100343_b18","series-title":"International Conference on Machine Learning","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","author":"Karimireddy","year":"2020"},{"key":"10.1016\/j.hcc.2025.100343_b19","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume":"33","author":"Fallah","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.hcc.2025.100343_b20","doi-asserted-by":"crossref","unstructured":"Bingyan Liu, Yao Guo, Xiangqun Chen, PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization, in: Proceedings of the Web Conference 2021, 2021, pp. 923\u2013934.","DOI":"10.1145\/3442381.3449847"},{"key":"10.1016\/j.hcc.2025.100343_b21","series-title":"Federated learning of a mixture of global and local models","author":"Hanzely","year":"2020"},{"issue":"9","key":"10.1016\/j.hcc.2025.100343_b22","first-page":"5149","article-title":"Meta-learning in neural networks: A survey","volume":"44","author":"Hospedales","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.hcc.2025.100343_b23","series-title":"Improving federated learning personalization via model agnostic meta learning","author":"Jiang","year":"2019"},{"key":"10.1016\/j.hcc.2025.100343_b24","article-title":"Adaptive gradient-based meta-learning methods","volume":"32","author":"Khodak","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.hcc.2025.100343_b25","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/OJCS.2023.3262203","article-title":"FLIS: Clustered federated learning via inference similarity for non-iid data distribution","volume":"4","author":"Morafah","year":"2023","journal-title":"IEEE Open J. Comput. Soc."},{"key":"10.1016\/j.hcc.2025.100343_b26","first-page":"1","article-title":"Clustered federated learning with adaptive local differential privacy on heterogeneous IoT data","author":"He","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.hcc.2025.100343_b27","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/OJCS.2023.3262203","article-title":"Flis: Clustered federated learning via inference similarity for non-iid data distribution","volume":"4","author":"Morafah","year":"2023","journal-title":"IEEE Open J. Comput. Soc."},{"key":"10.1016\/j.hcc.2025.100343_b28","series-title":"FedGroup: Accurate federated learning via decomposed similarity-based clustering","author":"Duan","year":"2020"},{"key":"10.1016\/j.hcc.2025.100343_b29","series-title":"Robust federated learning in a heterogeneous environment","author":"Ghosh","year":"2019"},{"issue":"2","key":"10.1016\/j.hcc.2025.100343_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.hcc.2023.100179","article-title":"FedQMIX: Communication-efficient federated learning via multi-agent reinforcement learning","volume":"4","author":"Cao","year":"2024","journal-title":"High- Confid. Comput."},{"issue":"4","key":"10.1016\/j.hcc.2025.100343_b31","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.26599\/BDMA.2024.9020065","article-title":"A remedy for heterogeneous data: Clustered federated learning with gradient trajectory","volume":"7","author":"Liu","year":"2024","journal-title":"Big Data Min. Anal."},{"issue":"1","key":"10.1016\/j.hcc.2025.100343_b32","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1109\/JIOT.2023.3299947","article-title":"Clustered federated learning with adaptive local differential privacy on heterogeneous iot data","volume":"11","author":"He","year":"2023","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"10.1016\/j.hcc.2025.100343_b33","doi-asserted-by":"crossref","first-page":"430","DOI":"10.26599\/BDMA.2024.9020068","article-title":"Federated transfer learning for on-device LLMs efficient fine tuning optimization","volume":"8","author":"Li","year":"2025","journal-title":"Big Data Min. Anal."},{"issue":"4","key":"10.1016\/j.hcc.2025.100343_b34","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MIS.2020.2988604","article-title":"Fedhealth: A federated transfer learning framework for wearable healthcare","volume":"35","author":"Chen","year":"2020","journal-title":"IEEE Intell. Syst."},{"issue":"2","key":"10.1016\/j.hcc.2025.100343_b35","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/TNSE.2020.2996612","article-title":"FedSteg: A federated transfer learning framework for secure image steganalysis","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"10.1016\/j.hcc.2025.100343_b36","article-title":"Client to server: Heterogeneous distribution knowledge transfer for federated learning","author":"Zhao","year":"2025","journal-title":"Tsinghua Sci. Technol."},{"issue":"1","key":"10.1016\/j.hcc.2025.100343_b37","doi-asserted-by":"crossref","first-page":"112","DOI":"10.26599\/TST.2023.9010156","article-title":"Ensemble knowledge distillation for federated semi-supervised image classification","volume":"30","author":"Shang","year":"2025","journal-title":"Tsinghua Sci. Technol."},{"key":"10.1016\/j.hcc.2025.100343_b38","first-page":"7865","article-title":"Personalized cross-silo federated learning on non-iid data","volume":"35","author":"Huang","year":"2021"},{"key":"10.1016\/j.hcc.2025.100343_b39","series-title":"Three approaches for personalization with applications to federated learning","author":"Mansour","year":"2020"},{"key":"10.1016\/j.hcc.2025.100343_b40","series-title":"Sparse federated learning with hierarchical personalized models","author":"Liu","year":"2022"},{"key":"10.1016\/j.hcc.2025.100343_b41","first-page":"11237","article-title":"Fedala: Adaptive local aggregation for personalized federated learning","volume":"37","author":"Zhang","year":"2023"},{"key":"10.1016\/j.hcc.2025.100343_b42","unstructured":"David Arthur, Sergei Vassilvitskii, K-Means++: The Advantages of Careful Seeding, in: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, New Orleans, Louisiana, USA, January 7-9, 2007, 2007."},{"issue":"11","key":"10.1016\/j.hcc.2025.100343_b43","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.hcc.2025.100343_b44","series-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"10.1016\/j.hcc.2025.100343_b45","series-title":"Deep learning for classical japanese literature","author":"Clanuwat","year":"2018"},{"key":"10.1016\/j.hcc.2025.100343_b46","series-title":"2023 IEEE International Performance, Computing, and Communications Conference","first-page":"397","article-title":"Adaptive edge-level personalization on hierarchical federated learning","author":"Song","year":"2023"}],"container-title":["High-Confidence Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667295225000479?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667295225000479?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T11:26:02Z","timestamp":1774869962000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2667295225000479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["S2667295225000479"],"URL":"https:\/\/doi.org\/10.1016\/j.hcc.2025.100343","relation":{},"ISSN":["2667-2952"],"issn-type":[{"value":"2667-2952","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CHPFL: Clustered adaptive hierarchical federated learning for edge-level personalization","name":"articletitle","label":"Article Title"},{"value":"High-Confidence Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.hcc.2025.100343","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V. on behalf of Shandong University.","name":"copyright","label":"Copyright"}],"article-number":"100343"}}