{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T02:02:18Z","timestamp":1780020138101,"version":"3.53.1"},"reference-count":33,"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","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","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.2026.116220","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T16:43:48Z","timestamp":1778777028000},"page":"116220","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["FedHPLP: A novel redundancy-aware aggregation method for horizontal federated learning"],"prefix":"10.1016","volume":"346","author":[{"given":"Tan","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xulei","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongxiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuaiyong","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9113-1414","authenticated-orcid":false,"given":"Chenghong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanying","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116220_bib0001","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1287\/ijoc.2022.0037","article-title":"Cost-effective acquisition of first-party data for business analytics","volume":"36","author":"Liu","year":"2024","journal-title":"Informs J. Comput."},{"key":"10.1016\/j.knosys.2026.116220_bib0002","doi-asserted-by":"crossref","first-page":"3342","DOI":"10.1038\/s41467-025-58549-0","article-title":"Achieving flexible fairness metrics in federated medical imaging","volume":"16","author":"Xing","year":"2025","journal-title":"Nat. Commun."},{"key":"10.1016\/j.knosys.2026.116220_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpe.2023.109095","article-title":"A federated machine learning approach for order-level risk prediction in supply chain financing","volume":"268","author":"Kong","year":"2024","journal-title":"Int. J. Prod. Econ."},{"key":"10.1016\/j.knosys.2026.116220_bib0004","article-title":"Leveraging asynchronous federated learning to predict customers financial distress","volume":"14","author":"Imteaj","year":"2022","journal-title":"Intell. Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116220_bib0005","series-title":"Federated Learning in Heterogeneous Networks with Unreliable Communication","first-page":"1","author":"Zheng","year":"2021"},{"key":"10.1016\/j.knosys.2026.116220_bib0006","series-title":"FedCDA: Federated learning with cross-rounds divergence-aware aggregation","author":"Wang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116220_bib0007","series-title":"DELTA: Diverse client sampling for fasting federated learning","author":"Wang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116220_bib0008","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"3069","article-title":"FedNLR: federated learning with neuron-wise learning rates","author":"Wang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116220_bib0009","series-title":"Not all samples are created equal: Deep learning with importance sampling","author":"Katharopoulos","year":"2018"},{"key":"10.1016\/j.knosys.2026.116220_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108919","article-title":"Instance weighted SMOTE by indirectly exploring the data distribution","volume":"249","author":"Zhang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116220_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106087","article-title":"A weighted hybrid ensemble method for classifying imbalanced data","volume":"203","author":"Zhao","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116220_bib0012","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.knosys.2026.116220_bib0013","series-title":"Class-balanced loss based on effective number of samples","first-page":"9260","author":"Cui","year":"2019"},{"key":"10.1016\/j.knosys.2026.116220_bib0014","doi-asserted-by":"crossref","first-page":"11521","DOI":"10.1109\/TPAMI.2023.3271451","article-title":"CMW-Net: Learning a class-aware sample weighting mapping for robust deep learning","volume":"45","author":"Shu","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116220_bib0015","series-title":"Robust importance weighting for covariate shift","author":"Lam","year":"2019"},{"key":"10.1016\/j.knosys.2026.116220_bib0016","series-title":"Importance weighted adversarial nets for partial domain adaptation","first-page":"8156","author":"Zhang","year":"2018"},{"key":"10.1016\/j.knosys.2026.116220_bib0017","series-title":"Distributionally robust neural networks","author":"Sagawa","year":"2020"},{"key":"10.1016\/j.knosys.2026.116220_bib0018","series-title":"Model-agnostic random weighting for out-of-distribution generalization","first-page":"1050","author":"He","year":"2024"},{"key":"10.1016\/j.knosys.2026.116220_bib0019","series-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2016"},{"key":"10.1016\/j.knosys.2026.116220_bib0020","series-title":"Local SGD: Unified theory and new efficient methods","first-page":"3556","author":"Gorbunov","year":"2021"},{"key":"10.1016\/j.knosys.2026.116220_bib0021","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TWC.2022.3190512","article-title":"Hierarchical federated learning with quantization: convergence analysis and system design","volume":"22","author":"Liu","year":"2023","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"10.1016\/j.knosys.2026.116220_bib0022","series-title":"FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization","author":"Reisizadeh","year":"2019"},{"key":"10.1016\/j.knosys.2026.116220_bib0023","series-title":"FedA3I: Annotation quality-aware aggregation for federated medical image segmentation against heterogeneous annotation noise","author":"Wu","year":"2024"},{"key":"10.1016\/j.knosys.2026.116220_bib0024","author":"Zhang"},{"key":"10.1016\/j.knosys.2026.116220_bib0025","series-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"20412","article-title":"DaFKD: domain-aware federated knowledge distillation","author":"Wang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116220_bib0026","doi-asserted-by":"crossref","first-page":"114183","DOI":"10.1016\/j.dss.2024.114183","article-title":"FedDQA: A novel regularization-based deep learning method for data quality assessment in federated learning","volume":"180","author":"Zhang","year":"2024","journal-title":"Decis. Support Syst."},{"key":"10.1016\/j.knosys.2026.116220_bib0027","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1109\/TPDS.2023.3250513","article-title":"From deterioration to acceleration: a calibration approach to rehabilitating step asynchronism in federated optimization","volume":"34","author":"Wu","year":"2023","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.knosys.2026.116220_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.127906","article-title":"Federated deep long-tailed learning: A survey","volume":"595","author":"Li","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.116220_bib0029","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"8432","article-title":"FedProto: federated prototype learning across heterogeneous clients","volume":"36","author":"Tan","year":"2022"},{"key":"10.1016\/j.knosys.2026.116220_bib0030","series-title":"BalanceFL: Addressing Class Imbalance in Long-Tail Federated Learning, 2022 21st ACM\/IEEE International Conference on Information Processing in Sensor Networks","first-page":"271","author":"Shuai","year":"2022"},{"key":"10.1016\/j.knosys.2026.116220_bib0031","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"3950","article-title":"Beyond Low-frequency information in graph convolutional networks","volume":"35","author":"Bo","year":"2021"},{"key":"10.1016\/j.knosys.2026.116220_bib0032","unstructured":"Cho, Y.J., Wang, J., & Joshi, G., Client selection in federated learning: Convergence analysis and power-of-choice selection strategies, arXiv preprint, (2020)."},{"key":"10.1016\/j.knosys.2026.116220_bib0033","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.neucom.2021.08.141","article-title":"FedSim: Similarity guided model aggregation for Federated Learning","volume":"483","author":"Palihawadana","year":"2022","journal-title":"Neurocomputing"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126009469?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126009469?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T01:10:58Z","timestamp":1780017058000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126009469"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":33,"alternative-id":["S0950705126009469"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116220","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":"FedHPLP: A novel redundancy-aware aggregation method for horizontal federated learning","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116220","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":"116220"}}