{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T14:27:00Z","timestamp":1764772020881,"version":"3.46.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Scientific and Technological Research Project in Henan Province","award":["252102210185","252102210185","252102210185","252102210185","252102210185","252102210185"],"award-info":[{"award-number":["252102210185","252102210185","252102210185","252102210185","252102210185","252102210185"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s10586-025-05755-6","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T16:16:25Z","timestamp":1760717785000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AHIBCS: an adaptive historical-information-based client selection algorithm for Non-IID data"],"prefix":"10.1007","volume":"28","author":[{"given":"Wei","family":"Liu","sequence":"first","affiliation":[]},{"given":"Xu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wentao","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Zhao","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Guangjun","family":"Zai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"5755_CR1","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.future.2023.09.008","volume":"150","author":"P Qi","year":"2024","unstructured":"Qi, P., Chiaro, D., Guzzo, A., Ianni, M., Fortino, G., Piccialli, F.: Model aggregation techniques in federated learning: a comprehensive survey. Futur. Gener. Comput. Syst. 150, 272\u2013293 (2024)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"5755_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2024.107683","volume":"166","author":"B Wang","year":"2025","unstructured":"Wang, B., Tian, Z., Ma, J., Zhang, W., She, W., Liu, W.: A decentralized asynchronous federated learning framework for edge devices. Future Generation Computer Systems 166, 107683 (2025)","journal-title":"Future Generation Computer Systems"},{"key":"5755_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128019","volume":"597","author":"B Liu","year":"2024","unstructured":"Liu, B., Lv, N., Guo, Y., Li, Y.: Recent advances on federated learning: a systematic survey. Neurocomputing 597, 128019 (2024). https:\/\/doi.org\/10.1016\/j.neucom.2024.128019","journal-title":"Neurocomputing"},{"issue":"3","key":"5755_CR4","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s10586-024-04759-y","volume":"28","author":"W Liu","year":"2025","unstructured":"Liu, W., Wang, Y., Li, K., Tian, Z., She, W.: Ftmoe: a federated transfer model based on mixture-of-experts for heterogeneous image classification. Clust. Comput. 28(3), 165 (2025)","journal-title":"Clust. Comput."},{"issue":"5","key":"5755_CR5","doi-asserted-by":"publisher","first-page":"6247","DOI":"10.1007\/s10586-024-04314-9","volume":"27","author":"B Tang","year":"2024","unstructured":"Tang, B., Xiao, Y., Zhang, L., Cao, B., Tang, M., Yang, Q.: Afl-hcs: asynchronous federated learning based on heterogeneous edge client selection. Cluster Computing 27(5), 6247\u20136264 (2024)","journal-title":"Cluster Computing"},{"issue":"3","key":"5755_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3625558","volume":"56","author":"M Ye","year":"2023","unstructured":"Ye, M., Fang, X., Du, B., Yuen, P.C., Tao, D.: Heterogeneous federated learning: state-of-the-art and research challenges. ACM Comput. Surv. 56(3), 1\u201344 (2023)","journal-title":"ACM Comput. Surv."},{"key":"5755_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112201","volume":"301","author":"Y Cong","year":"2024","unstructured":"Cong, Y., Zeng, Y., Qiu, J., Fang, Z., Zhang, L., Cheng, D., Liu, J., Tian, Z.: Fedga: a greedy approach to enhance federated learning with non-iid data. Knowl.-Based Syst. 301, 112201 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"5755_CR8","doi-asserted-by":"crossref","unstructured":"Tang, M., Ning, X., Wang, Y., Sun, J., Wang, Y., Li, H., Chen, Y.: Fedcor: Correlation-based active client selection strategy for heterogeneous federated learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10102\u201310111 (2022)","DOI":"10.1109\/CVPR52688.2022.00986"},{"key":"5755_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.112962","volume":"310","author":"B Wang","year":"2025","unstructured":"Wang, B., Tian, Z., Liu, X., Xia, Y., She, W., Liu, W.: A multi-center federated learning mechanism based on consortium blockchain for data secure sharing. Knowledge-Based Systems 310, 112962 (2025)","journal-title":"Knowledge-Based Systems"},{"issue":"2","key":"5755_CR10","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1007\/s10586-024-04846-0","volume":"28","author":"L Hu","year":"2025","unstructured":"Hu, L., Hu, Y., Jiang, L., Long, W.: Federated learning client selection algorithm based on gradient similarity. Clust. Comput. 28(2), 134 (2025)","journal-title":"Clust. Comput."},{"issue":"4","key":"5755_CR11","doi-asserted-by":"publisher","first-page":"3997","DOI":"10.1007\/s10586-024-04273-1","volume":"27","author":"Y Abuzied","year":"2024","unstructured":"Abuzied, Y., Ghanem, M., Dawoud, F., Gamal, H., Soliman, E., Sharara, H., Elbatt, T.: A privacy-preserving federated learning framework for blockchain networks. Clust. Comput 27(4), 3997\u20134014 (2024)","journal-title":"Clust. Comput"},{"issue":"11","key":"5755_CR12","doi-asserted-by":"publisher","first-page":"17095","DOI":"10.1109\/JIOT.2025.3538887","volume":"12","author":"B Wang","year":"2025","unstructured":"Wang, B., Tian, Z., Tang, F., Pan, H., She, W., Liu, W.: Blockchain-empowered asynchronous federated reinforcement learning for iot-based traffic trajectory prediction. IEEE Internet Things J 12(11), 17095\u201317109 (2025). https:\/\/doi.org\/10.1109\/JIOT.2025.3538887","journal-title":"IEEE Internet Things J"},{"issue":"2","key":"5755_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04834-4","volume":"28","author":"M Asad","year":"2025","unstructured":"Asad, M., Otoum, S.: Bppfl: a blockchain-based framework for privacy-preserving federated learning. Cluster Computing 28(2), 126 (2025)","journal-title":"Cluster Computing"},{"issue":"3","key":"5755_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3385440","volume":"70","author":"J Pei","year":"2024","unstructured":"Pei, J., Liu, W., Li, J., Wang, L., Liu, C.: A review of federated learning methods in heterogeneous scenarios. IEEE Trans. Consum. Electron. 70(3), 5983\u20135999 (2024). https:\/\/doi.org\/10.1109\/TCE.2024.3385440","journal-title":"IEEE Trans. Consum. Electron."},{"key":"5755_CR15","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.future.2022.05.003","volume":"135","author":"X Ma","year":"2022","unstructured":"Ma, X., Zhu, J., Lin, Z., Chen, S., Qin, Y.: A state-of-the-art survey on solving non-iid data in federated learning. Futur. Gener. Comput. Syst. 135, 244\u2013258 (2022)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"5755_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2024.104990","volume":"195","author":"Z Li","year":"2025","unstructured":"Li, Z., Yuan, S., Guan, Z.: Robust and scalable federated learning framework for client data heterogeneity based on optimal clustering. Journal of Parallel and Distributed Computing 195, 104990 (2025)","journal-title":"Journal of Parallel and Distributed Computing"},{"key":"5755_CR17","doi-asserted-by":"crossref","unstructured":"Tan, Y., Long, G., Liu, L., Zhou, T., Lu, Q., Jiang, J., Zhang, C.: Fedproto: Federated prototype learning across heterogeneous clients. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 8432\u20138440 (2022)","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"5755_CR18","doi-asserted-by":"crossref","unstructured":"Kwatra, S., Torra, V.: A k-anonymised federated learning framework with decision trees. In: International Workshop on Data Privacy Management, pp. 106\u2013120 (2021). Springer","DOI":"10.1007\/978-3-030-93944-1_7"},{"key":"5755_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.109067","volume":"114","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Wang, W., Wang, X., Zhang, H., Wu, X., Yang, M.: Fedtweet: two-fold knowledge distillation for non-iid federated learning. Computers and Electrical Engineering 114, 109067 (2024)","journal-title":"Computers and Electrical Engineering"},{"key":"5755_CR20","doi-asserted-by":"crossref","unstructured":"Wu, J., Liu, Q., Huang, Z., Ning, Y., Wang, H., Chen, E., Yi, J., Zhou, B.: Hierarchical personalized federated learning for user modeling. In: Proceedings of the Web Conference 2021, pp. 957\u2013968 (2021)","DOI":"10.1145\/3442381.3449926"},{"key":"5755_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2024.110663","volume":"251","author":"J Li","year":"2024","unstructured":"Li, J., Chen, T., Teng, S.: A comprehensive survey on client selection strategies in federated learning. Comput. Netw. 251, 110663 (2024). https:\/\/doi.org\/10.1016\/j.comnet.2024.110663","journal-title":"Comput. Netw."},{"issue":"24","key":"5755_CR22","doi-asserted-by":"publisher","first-page":"21811","DOI":"10.1109\/JIOT.2023.3299573","volume":"10","author":"L Fu","year":"2023","unstructured":"Fu, L., Zhang, H., Gao, G., Zhang, M., Liu, X.: Client selection in federated learning: principles, challenges, and opportunities. IEEE Internet Things J 10(24), 21811\u201321819 (2023)","journal-title":"IEEE Internet Things J"},{"key":"5755_CR23","doi-asserted-by":"crossref","unstructured":"Smestad, C., Li, J.: A systematic literature review on client selection in federated learning. In: Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, pp. 2\u201311 (2023)","DOI":"10.1145\/3593434.3593438"},{"issue":"2","key":"5755_CR24","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1007\/s11831-023-10011-4","volume":"31","author":"S Mayhoub","year":"2024","unstructured":"Mayhoub, S., M. Shami, T.: A review of client selection methods in federated learning. Archives of Computational Methods in Engineering 31(2), 1129\u20131152 (2024)","journal-title":"Archives of Computational Methods in Engineering"},{"issue":"12","key":"5755_CR25","doi-asserted-by":"publisher","first-page":"14934","DOI":"10.1109\/TMC.2024.3450549","volume":"23","author":"L Dong","year":"2024","unstructured":"Dong, L., Zhou, Y., Liu, L., Qi, Y., Zhang, Y.: Age of information based client selection for wireless federated learning with diversified learning capabilities. IEEE Trans. Mob. Comput. 23(12), 14934\u201314945 (2024). https:\/\/doi.org\/10.1109\/TMC.2024.3450549","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"1","key":"5755_CR26","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1109\/TSC.2024.3350050","volume":"17","author":"W Mao","year":"2024","unstructured":"Mao, W., Lu, X., Jiang, Y., Zheng, H.: Joint client selection and bandwidth allocation of wireless federated learning by deep reinforcement learning. IEEE Trans. Serv. Comput. 17(1), 336\u2013348 (2024)","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"6","key":"5755_CR27","doi-asserted-by":"publisher","first-page":"8835","DOI":"10.1109\/TVT.2024.3359860","volume":"73","author":"F Zheng","year":"2024","unstructured":"Zheng, F., Sun, Y., Ni, B.: Fedaeb: deep reinforcement learning based joint client selection and resource allocation strategy for heterogeneous federated learning. IEEE Trans. Veh. Technol. 73(6), 8835\u20138846 (2024)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"5755_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3396279","author":"C Xu","year":"2024","unstructured":"Xu, C., Liu, H., Li, K., Feng, W., Qi, W.: Pretraining client selection algorithm based on a data distribution evaluation model in federated learning. IEEE Access (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3396279","journal-title":"IEEE Access"},{"key":"5755_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128672","volume":"612","author":"Z Pan","year":"2025","unstructured":"Pan, Z., Li, Y., Guan, Z., Liang, M., Li, A., Wang, J., Kou, F.: Rfcsc: communication efficient reinforcement federated learning with dynamic client selection and adaptive gradient compression. Neurocomputing 612, 128672 (2025)","journal-title":"Neurocomputing"},{"key":"5755_CR30","unstructured":"Cho, Y.J., Wang, J., Joshi, G.: Client selection in federated learning: Convergence analysis and power-of-choice selection strategies. arXiv preprint arXiv:2010.01243 (2020)"},{"issue":"2","key":"5755_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04850-4","volume":"28","author":"CC Nikolaidis","year":"2025","unstructured":"Nikolaidis, C.C., Efraimidis, P.S.: Advancing elderly social care dropout prediction with federated learning: client selection and imbalanced data management. Cluster Computing 28(2), 114 (2025)","journal-title":"Cluster Computing"},{"key":"5755_CR32","doi-asserted-by":"crossref","unstructured":"Sivasubramanian, D., Nagalapatti, L., Iyer, R., Ramakrishnan, G.: Gradient coreset for federated learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2648\u20132657 (2024)","DOI":"10.1109\/WACV57701.2024.00263"},{"issue":"5","key":"5755_CR33","doi-asserted-by":"publisher","first-page":"4953","DOI":"10.1109\/JIOT.2024.3524389","volume":"12","author":"J Tan","year":"2025","unstructured":"Tan, J., Liu, Z., Guo, K., Zhao, M.: Long-term client selection for federated learning with non-iid data: a truthful auction approach. IEEE Internet Things J 12(5), 4953\u20134970 (2025). https:\/\/doi.org\/10.1109\/JIOT.2024.3524389","journal-title":"IEEE Internet Things J"},{"issue":"2","key":"5755_CR34","first-page":"1","volume":"20","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Wang, J., Li, Y., Xin, F., Dong, F., Luo, J., Wu, Z.: Addressing heterogeneity in federated learning with client selection via submodular optimization. ACM Transactions on Sensor Networks 20(2), 1\u201332 (2024)","journal-title":"ACM Transactions on Sensor Networks"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05755-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05755-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05755-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T14:15:59Z","timestamp":1764771359000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05755-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":34,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["5755"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05755-6","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2025,10,17]]},"assertion":[{"value":"10 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1036"}}