{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:28:55Z","timestamp":1772166535809,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100018568","name":"Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province","doi-asserted-by":"publisher","award":["Hainan Province Science and Technology Special Fund under Grant"],"award-info":[{"award-number":["Hainan Province Science and Technology Special Fund under Grant"]}],"id":[{"id":"10.13039\/501100018568","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Wireless Com Network"],"DOI":"10.1186\/s13638-025-02511-7","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T09:38:50Z","timestamp":1760521130000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A federated meta-learning aided intelligent edge framework by using the parameter optimization approach"],"prefix":"10.1186","volume":"2025","author":[{"given":"Zhu","family":"Xiaofeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Qiaosong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Jiaqiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Yuwen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"2511_CR1","doi-asserted-by":"publisher","first-page":"53040","DOI":"10.1109\/ACCESS.2019.2912200","volume":"7","author":"A Shrestha","year":"2019","unstructured":"A. Shrestha, A. Mahmood, Review of deep learning algorithms and architectures. IEEE Access 7, 53040\u201353065 (2019)","journal-title":"IEEE Access"},{"issue":"3","key":"2511_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/MIS.2022.3184260","volume":"37","author":"L Cao","year":"2022","unstructured":"L. Cao, Deep learning applications. IEEE Intell. Syst. 37(3), 3\u20135 (2022)","journal-title":"IEEE Intell. Syst."},{"key":"2511_CR3","doi-asserted-by":"crossref","unstructured":"F. Y\u0131ld\u0131r\u0131m, Y. Yalman, K.C. Bay\u0131nd\u0131r , E. Terciyanl\u0131, Comprehensive review of edge computing for power systems: State of the art, architecture, and applications. Applied Sciences 15(8) (2025)","DOI":"10.3390\/app15084592"},{"issue":"12","key":"2511_CR4","doi-asserted-by":"publisher","first-page":"11406","DOI":"10.1109\/TMC.2024.3398801","volume":"23","author":"Y Xu","year":"2024","unstructured":"Y. Xu, Y. Liao, L. Wang, H. Xu, Z. Jiang, W. Zhang, Overcoming noisy labels and non-IID data in edge federated learning. IEEE Trans. Mob. Comput. 23(12), 11406\u201311421 (2024)","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"4","key":"2511_CR5","doi-asserted-by":"publisher","first-page":"2892","DOI":"10.1109\/COMST.2023.3316615","volume":"25","author":"Q Duan","year":"2023","unstructured":"Q. Duan, J. Huang, S. Hu, R. Deng, Z. Lu, S. Yu, Combining federated learning and edge computing toward ubiquitous intelligence in 6g network: challenges, recent advances, and future directions. IEEE Commun. Surv. Tutorials 25(4), 2892\u20132950 (2023)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"2511_CR6","doi-asserted-by":"crossref","unstructured":"Z. Xu, Federated learning in the age of foundation models. In: 2024 2nd International Conference on Federated Learning Technologies and Applications (FLTA), 2\u20132 (2024)","DOI":"10.1109\/FLTA63145.2024.10840123"},{"issue":"7","key":"2511_CR7","doi-asserted-by":"publisher","first-page":"5476","DOI":"10.1109\/JIOT.2020.3030072","volume":"8","author":"S AbdulRahman","year":"2020","unstructured":"S. AbdulRahman, H. Tout, H. Ould-Slimane et al., A survey on federated learning: the journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J. 8(7), 5476\u20135497 (2020)","journal-title":"IEEE Internet Things J."},{"key":"2511_CR8","doi-asserted-by":"crossref","unstructured":"J. Jang, B.J. Choi, Personalized federated learning via deviation tracking representation learning. 2024 International Conference on Information Networking (ICOIN), 762\u2013766 (2024)","DOI":"10.1109\/ICOIN59985.2024.10572208"},{"key":"2511_CR9","unstructured":"P. Kairouz, H.B. McMahan, B. Avent, et al. Advances and open problems in federated learning. Foundations and Trends\u00ae in Machine Learning 14(1\u20132) 1\u2013210 (2021)"},{"issue":"3","key":"2511_CR10","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"T. Li, A.K. Sahu, A. Talwalkar et al., Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37(3), 50\u201360 (2020)","journal-title":"IEEE Signal Process. Mag."},{"key":"2511_CR11","unstructured":"Z. Zhou. Research on efficient federated learning algorithm for communication based on model compression. PhD thesis, Wuhan University of Engineering, Hubei (2023)"},{"key":"2511_CR12","unstructured":"Y. Zhao, M. Li, L. Lai, et al.: Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 (2018)"},{"key":"2511_CR13","doi-asserted-by":"crossref","unstructured":"N. Yoshida, T. Nishio, M. Morikura, et al: Hybrid-fl for wireless networks: cooperative learning mechanism using non-iid data. ICC 2020-2020 IEEE International Conference on Communications (ICC), pp. 1\u20137. (IEEE, 2020)","DOI":"10.1109\/ICC40277.2020.9149323"},{"issue":"2","key":"2511_CR14","doi-asserted-by":"publisher","first-page":"122","DOI":"10.11959\/j.issn.2096-0271.2022051","volume":"9","author":"C-Y Zhang","year":"2023","unstructured":"C.-Y. Zhang, S.-J. Si, J.-Z. Wang et al., A review of federated meta-learning. Big Data 9(2), 122\u2013146 (2023). https:\/\/doi.org\/10.11959\/j.issn.2096-0271.2022051","journal-title":"Big Data"},{"key":"2511_CR15","unstructured":"P. Zhou, X.-T. Yuan, W. Xu et al. Efficient meta learning via minibatch proximal update. Adv. Neural. Inf. Process. Syst. 32, (2019)"},{"key":"2511_CR16","unstructured":"J.Schmidhuber. Evolutionary principles in self-referential learning. PhD thesis, Technische Universit\u00e4t M\u00fcnchen (1987)"},{"key":"2511_CR17","doi-asserted-by":"crossref","unstructured":"Y. Bengio, S. Bengio, J.Cloutier. Learning a synaptic learning rule. In: International Joint Conference on Neural Networks (IJCNN) (1990)","DOI":"10.1109\/IJCNN.1991.155621"},{"issue":"9","key":"2511_CR18","first-page":"5149","volume":"44","author":"T Hospedales","year":"2022","unstructured":"T. Hospedales, A. Antoniou, P. Micaelli, A. Storkey, Meta-learning in neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5149\u20135169 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2511_CR19","doi-asserted-by":"crossref","unstructured":"G.K.Michelon, W.K.G. Assun\u00e7\u00e3o, P.Gr\u00fcnbacher, A.Egyed, Analysis and propagation of feature revisions in preprocessor-based software product lines. In: 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 284\u2013295 (2023)","DOI":"10.1109\/SANER56733.2023.00035"},{"issue":"1","key":"2511_CR20","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1109\/TSG.2023.3263814","volume":"15","author":"Q Luo","year":"2024","unstructured":"Q. Luo, T. Yu, C. Lan, Y. Huang, Z. Wang, Z. Pan, A generalizable method for practical non-intrusive load monitoring via metric-based meta-learning. IEEE Trans. Smart Grid 15(1), 1103\u20131115 (2024)","journal-title":"IEEE Trans. Smart Grid"},{"issue":"6","key":"2511_CR21","doi-asserted-by":"publisher","first-page":"5209","DOI":"10.1109\/TCYB.2020.3028378","volume":"52","author":"Z Xu","year":"2022","unstructured":"Z. Xu, X. Chen, L. Cao, Fast task adaptation based on the combination of model-based and gradient-based meta learning. IEEE Trans. Cybernetics 52(6), 5209\u20135218 (2022)","journal-title":"IEEE Trans. Cybernetics"},{"key":"2511_CR22","unstructured":"A.Santoro, S.Bartunov, M.Botvinick, et al: Meta-learning with memory-augmented neural networks. In: International Conference on Machine Learning, (PMLR, 2016), pp. 1842\u20131850"},{"key":"2511_CR23","unstructured":"C.Finn, P.Abbeel, S.Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, (PMLR, 2017), pp. 1126\u20131135"},{"key":"2511_CR24","unstructured":"A. Nichol, J. Achiam, J.Schulman, On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 (2018)"},{"key":"2511_CR25","unstructured":"G. Denevi, C. Ciliberto, D. Stamos, et al.: Learning to learn around a common mean. Advances in Neural Information Processing Systems 31 (2018)"},{"key":"2511_CR26","unstructured":"M.-F.F. Balcan, M. Khodak, A. Talwalkar et al. Provable guarantees for gradient-based meta-learning. International Conference on Machine Learning (PMLR, 2019), pp. 424\u2013433"},{"issue":"1","key":"2511_CR27","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/TVCG.2024.3456371","volume":"31","author":"A Boggust","year":"2025","unstructured":"A. Boggust, V. Sivaraman, Y. Assogba, D. Ren, D. Moritz, F. Hohman, Compress and compare: interactively evaluating efficiency and behavior across ml model compression experiments. IEEE Trans. Visual Comput. Graphics 31(1), 809\u2013819 (2025)","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"issue":"9","key":"2511_CR28","doi-asserted-by":"publisher","first-page":"6013","DOI":"10.1109\/TII.2019.2953106","volume":"16","author":"S Fu","year":"2020","unstructured":"S. Fu, Z. Li, K. Liu, S. Din, M. Imran, X. Yang, Model compression for IoT applications in industry 4.0 via multiscale knowledge transfer. IEEE Trans. Industr. Inf. 16(9), 6013\u20136022 (2020)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"2511_CR29","doi-asserted-by":"crossref","unstructured":"Z. Qin, G. Feng, Y. Liu, T.P. Yum, F. Wang, J. Wang, Efficient federated learning in wireless networks with incremental model quantization and uploading. IEEE Transactions on Network Science and Engineering (2025), pp. 1\u201314","DOI":"10.1109\/TNSE.2025.3546333"},{"key":"2511_CR30","unstructured":"Y. Gong, L. Liu, M. Yang, et al.: Compressing deep convolutional networks using vector quantization. arXiv preprint arXiv:1412.6115 (2014)"},{"key":"2511_CR31","unstructured":"J. Kim, S.W. Park, N. Kwak. Paraphrasing complex network: Network compression via factor transfer. Advances in neural information processing systems 31 (2018)"},{"key":"2511_CR32","doi-asserted-by":"crossref","unstructured":"N. Passalis, A. Tefas, Learning deep representations with probabilistic knowledge transfer. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018), pp. 268\u2013284","DOI":"10.1007\/978-3-030-01252-6_17"},{"issue":"6","key":"2511_CR33","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1109\/JSAC.2019.2904348","volume":"37","author":"S Wang","year":"2019","unstructured":"S. Wang, T. Tuor, T. Salonidis et al., Adaptive federated learning in resource constrained edge computing systems. IEEE J. Sel. Areas Commun. 37(6), 1205\u20131221 (2019)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"2511_CR34","unstructured":"B. McMahan, E. Moore, D. Ramage et al., Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics (PMLR, 2017), pp. 1273\u20131282"}],"container-title":["EURASIP Journal on Wireless Communications and Networking"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-025-02511-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13638-025-02511-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-025-02511-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T09:39:01Z","timestamp":1760521141000},"score":1,"resource":{"primary":{"URL":"https:\/\/jwcn-eurasipjournals.springeropen.com\/articles\/10.1186\/s13638-025-02511-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2511"],"URL":"https:\/\/doi.org\/10.1186\/s13638-025-02511-7","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6819153\/v1","asserted-by":"object"}]},"ISSN":["1687-1499"],"issn-type":[{"value":"1687-1499","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,15]]},"assertion":[{"value":"9 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2025","order":3,"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 that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"84"}}