{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T12:17:52Z","timestamp":1783167472773,"version":"3.54.6"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62572110"],"award-info":[{"award-number":["62572110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U25A20431"],"award-info":[{"award-number":["U25A20431"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62432003"],"award-info":[{"award-number":["62432003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62572108"],"award-info":[{"award-number":["62572108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.knosys.2026.116332","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T15:56:42Z","timestamp":1780070202000},"page":"116332","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DuaFed: A clustered federated learning framework via dual-domain feature alignment for tackling data heterogeneity"],"prefix":"10.1016","volume":"347","author":[{"given":"Shining","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongfei","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116332_b1","article-title":"A survey on deep learning in edge-cloud collaboration: Model partitioning, privacy preservation, and prospects","author":"Zhang","year":"2025","journal-title":"Knowl.-Based Syst."},{"issue":"6","key":"10.1016\/j.knosys.2026.116332_b2","doi-asserted-by":"crossref","first-page":"5112","DOI":"10.1109\/TNET.2024.3450098","article-title":"Jointcloud resource market competition: a game-theoretic approach","volume":"32","author":"Shi","year":"2024","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"10.1016\/j.knosys.2026.116332_b3","doi-asserted-by":"crossref","first-page":"107486","DOI":"10.1016\/j.future.2024.107486","article-title":"Jcdc: a blockchain-based framework for secure data storage and circulation in jointcloud","volume":"162","author":"Zhang","year":"2025","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.knosys.2026.116332_b4","doi-asserted-by":"crossref","first-page":"104221","DOI":"10.1016\/j.jnca.2025.104221","article-title":"Pmmjc: a preference-based multi-stage matching-mechanism for jointcloud environments","volume":"242","author":"Lu","year":"2025","journal-title":"J. Netw Comput. Appl."},{"key":"10.1016\/j.knosys.2026.116332_b5","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.knosys.2026.116332_b6","series-title":"2022 IEEE 38th International Conference on Data Engineering","first-page":"965","article-title":"Federated learning on non-iid data silos: An experimental study","author":"Li","year":"2022"},{"key":"10.1016\/j.knosys.2026.116332_b7","article-title":"Federated learning with non-iid data: A survey","author":"Lu","year":"2024","journal-title":"IEEE Internet Things J."},{"issue":"8","key":"10.1016\/j.knosys.2026.116332_b8","doi-asserted-by":"crossref","first-page":"3710","DOI":"10.1109\/TNNLS.2020.3015958","article-title":"Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints","volume":"32","author":"Sattler","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.knosys.2026.116332_b9","series-title":"ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"8861","article-title":"On the byzantine robustness of clustered federated learning","author":"Sattler","year":"2020"},{"key":"10.1016\/j.knosys.2026.116332_b10","series-title":"2020 International Joint Conference on Neural Networks","first-page":"1","article-title":"Federated learning with hierarchical clustering of local updates to improve training on non-IID data","author":"Briggs","year":"2020"},{"key":"10.1016\/j.knosys.2026.116332_b11","series-title":"2023 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops)","first-page":"614","article-title":"DCFL: Dynamic clustered federated learning under differential privacy settings","author":"Augello","year":"2023"},{"issue":"12","key":"10.1016\/j.knosys.2026.116332_b12","doi-asserted-by":"crossref","first-page":"11856","DOI":"10.1109\/TII.2023.3252599","article-title":"A greedy agglomerative framework for clustered federated learning","volume":"19","author":"Mehta","year":"2023","journal-title":"IEEE Trans. Ind. Informatics"},{"issue":"3","key":"10.1016\/j.knosys.2026.116332_b13","doi-asserted-by":"crossref","first-page":"4038","DOI":"10.1109\/JSYST.2023.3243694","article-title":"FedSeq: A hybrid federated learning framework based on sequential in-cluster training","volume":"17","author":"Chen","year":"2023","journal-title":"IEEE Syst. J."},{"key":"10.1016\/j.knosys.2026.116332_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110741","article-title":"A dynamic adaptive iterative clustered federated learning scheme","volume":"276","author":"Du","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116332_b15","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.neunet.2023.06.010","article-title":"Contrastive encoder pre-training-based clustered federated learning for heterogeneous data","volume":"165","author":"Tun","year":"2023","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2026.116332_b16","series-title":"2024 IEEE International Conference on Big Data (BigData)","first-page":"8108","article-title":"One-shot clustering for federated learning","author":"Zuziak","year":"2024"},{"issue":"2","key":"10.1016\/j.knosys.2026.116332_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3298981","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"issue":"7","key":"10.1016\/j.knosys.2026.116332_b18","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":"2020","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.knosys.2026.116332_b19","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.knosys.2026.116332_b20","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.knosys.2026.116332_b21","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume":"33","author":"Wang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116332_b22","doi-asserted-by":"crossref","unstructured":"M. Mendieta, T. Yang, P. Wang, M. Lee, Z. Ding, C. Chen, Local learning matters: Rethinking data heterogeneity in federated learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 8397\u20138406.","DOI":"10.1109\/CVPR52688.2022.00821"},{"key":"10.1016\/j.knosys.2026.116332_b23","article-title":"Communication-efficient federated learning for heterogeneous clients","author":"Li","year":"2025","journal-title":"ACM Trans. Internet Technol."},{"key":"10.1016\/j.knosys.2026.116332_b24","first-page":"19586","article-title":"An efficient framework for clustered federated learning","volume":"33","author":"Ghosh","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116332_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.knosys.2026.116332_b26","series-title":"Fedac: An adaptive clustered federated learning framework for heterogeneous data","author":"Zhang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116332_b27","series-title":"ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1","article-title":"Lcfed: An efficient clustered federated learning framework for heterogeneous data","author":"Zhang","year":"2025"},{"key":"10.1016\/j.knosys.2026.116332_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107278","article-title":"Stocfl: A stochastically clustered federated learning framework for non-IID data with dynamic client participation","volume":"187","author":"Zeng","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2026.116332_b29","first-page":"18593","article-title":"EBS-cfl: Efficient and Byzantine-robust secure clustered federated learning","volume":"vol. 39","author":"Li","year":"2025"},{"key":"10.1016\/j.knosys.2026.116332_b30","doi-asserted-by":"crossref","unstructured":"C. Zhang, Y. Xu, J. Tan, J. An, W. Jin, MingledPie: A Cluster Mingling Approach for Mitigating Preference Profiling in CFL., in: NDSS, 2025.","DOI":"10.14722\/ndss.2025.240195"},{"key":"10.1016\/j.knosys.2026.116332_b31","doi-asserted-by":"crossref","unstructured":"B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 1992, pp. 144\u2013152.","DOI":"10.1145\/130385.130401"},{"key":"10.1016\/j.knosys.2026.116332_b32","volume":"vol. 152","author":"Paulsen","year":"2016"},{"key":"10.1016\/j.knosys.2026.116332_b33","series-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"10.1016\/j.knosys.2026.116332_b34","series-title":"Cinic-10 is not imagenet or cifar-10","author":"Darlow","year":"2018"},{"issue":"7","key":"10.1016\/j.knosys.2026.116332_b35","first-page":"3","article-title":"Tiny imagenet visual recognition challenge","volume":"7","author":"Le","year":"2015","journal-title":"CS 231N"},{"key":"10.1016\/j.knosys.2026.116332_b36","first-page":"8124","article-title":"Fedsoft: Soft clustered federated learning with proximal local updating","volume":"vol. 36","author":"Ruan","year":"2022"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126010580?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126010580?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T11:40:37Z","timestamp":1783165237000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126010580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":36,"alternative-id":["S0950705126010580"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116332","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DuaFed: A clustered federated learning framework via dual-domain feature alignment for tackling data heterogeneity","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116332","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116332"}}