{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T15:38:29Z","timestamp":1761925109508,"version":"build-2065373602"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s10586-025-05311-2","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T19:37:19Z","timestamp":1757965039000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ACFFSL:a federated few-shot learning framework with contrastive learning and lightweight multi-scale attention"],"prefix":"10.1007","volume":"28","author":[{"given":"Chao","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiyuan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajia","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Defeng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"5311_CR1","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/978-3-031-77464-5_9","volume-title":"Artificial Intelligence-Based Games as Novel Holistic Educational Environments to Teach 21st Century Skills","author":"S Papadimitriou","year":"2025","unstructured":"Papadimitriou, S., Virvou, M.: General data protection regulation and adaptive educational games. In: Artificial Intelligence-Based Games as Novel Holistic Educational Environments to Teach 21st Century Skills, pp. 253\u2013275. Springer, New York (2025)"},{"key":"5311_CR2","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282 (2017). PMLR"},{"key":"5311_CR3","unstructured":"Li, X., Jiang, M., Zhang, X., Kamp, M., Dou, Q.: FedBN: Federated learning on non-IID features via local batch normalization. arXiv preprint arXiv:2102.07623 (2021)"},{"issue":"9","key":"5311_CR4","doi-asserted-by":"publisher","first-page":"12727","DOI":"10.1007\/s10586-024-04558-5","volume":"27","author":"Q Zhou","year":"2024","unstructured":"Zhou, Q., Shen, W.: PPFLV: privacy-preserving federated learning with verifiability. Clust. Comput. 27(9), 12727\u201312743 (2024)","journal-title":"Clust. Comput."},{"key":"5311_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102572","volume":"141","author":"A Heidari","year":"2023","unstructured":"Heidari, A., Javaheri, D., Toumaj, S., Navimipour, N.J., Rezaei, M., Unal, M.: A new lung cancer detection method based on the chest CT images using federated learning and blockchain systems. Artif. Intell. Med. 141, 102572 (2023)","journal-title":"Artif. Intell. Med."},{"issue":"13s","key":"5311_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3582688","volume":"55","author":"Y Song","year":"2023","unstructured":"Song, Y., Wang, T., Cai, P., Mondal, S.K., Sahoo, J.P.: A comprehensive survey of few-shot learning: evolution, applications, challenges, and opportunities. ACM Comput. Surv. 55(13s), 1\u201340 (2023)","journal-title":"ACM Comput. Surv."},{"key":"5311_CR7","doi-asserted-by":"crossref","unstructured":"Simon, C., Koniusz, P., Nock, R., Harandi, M.: Adaptive subspaces for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4136\u20134145 (2020)","DOI":"10.1109\/CVPR42600.2020.00419"},{"key":"5311_CR8","doi-asserted-by":"crossref","unstructured":"Xie, J., Long, F., Lv, J., Wang, Q., Li, P.: Joint distribution matters: deep brownian distance covariance for few-shot classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7972\u20137981 (2022)","DOI":"10.1109\/CVPR52688.2022.00781"},{"key":"5311_CR9","unstructured":"Yang, S., Liu, L., Xu, M.: Free lunch for few-shot learning: distribution calibration. arXiv preprint arXiv:2101.06395 (2021)"},{"key":"5311_CR10","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126\u20131135 (2017). PMLR"},{"key":"5311_CR11","first-page":"11237","volume":"37","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Hua, Y., Wang, H., Song, T., Xue, Z., Ma, R., Guan, H.: FedALA: Adaptive local aggregation for personalized federated learning. Proc. AAAI Conf. Artif. Intell. 37, 11237\u201311244 (2023)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"5311_CR12","first-page":"8432","volume":"36","author":"Y Tan","year":"2022","unstructured":"Tan, Y., Long, G., Liu, L., Zhou, T., Lu, Q., Jiang, J., Zhang, C.: FedProto: federated prototype learning across heterogeneous clients. Proc. AAAI Conf. Artif. Intell. 36, 8432\u20138440 (2022)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"5311_CR13","first-page":"587","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"X-C Li","year":"2021","unstructured":"Li, X.-C., Zhan, D.-C., Shao, Y., Li, B., Song, S.: FedPHP: Federated personalization with inherited private models. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 587\u2013602. Springer, New York (2021)"},{"key":"5311_CR14","doi-asserted-by":"crossref","unstructured":"Wang, S., Fu, X., Ding, K., Chen, C., Chen, H., Li, J.: Federated few-shot learning. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2374\u20132385 (2023)","DOI":"10.1145\/3580305.3599347"},{"issue":"2","key":"5311_CR15","doi-asserted-by":"publisher","first-page":"2534","DOI":"10.1109\/TNNLS.2022.3190359","volume":"35","author":"Y Zhao","year":"2022","unstructured":"Zhao, Y., Yu, G., Wang, J., Domeniconi, C., Guo, M., Zhang, X., Cui, L.: Personalized federated few-shot learning. IEEE Trans. Neural Netw. Learn. Syst. 35(2), 2534\u20132544 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"5311_CR16","doi-asserted-by":"crossref","unstructured":"Ahn, H., Kwak, J., Lim, S., Bang, H., Kim, H., Moon, T.: SS-IL: Separated softmax for incremental learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 844\u2013853 (2021)","DOI":"10.1109\/ICCV48922.2021.00088"},{"key":"5311_CR17","doi-asserted-by":"crossref","unstructured":"Dong, J., Wang, L., Fang, Z., Sun, G., Xu, S., Wang, X., Zhu, Q.: Federated class-incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10164\u201310173 (2022)","DOI":"10.1109\/CVPR52688.2022.00992"},{"key":"5311_CR18","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"5311_CR19","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"5311_CR20","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"5311_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, Q., Zheng, Z., Cheng, M.-M., Yang, J., Li, X.: Large selective kernel network for remote sensing object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16794\u201316805 (2023)","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"5311_CR22","doi-asserted-by":"crossref","unstructured":"Si, Y., Xu, H., Zhu, X., Zhang, W., Dong, Y., Chen, Y., Li, H.: SCSA: Exploring the synergistic effects between spatial and channel attention. arXiv preprint arXiv:2407.05128 (2024)","DOI":"10.1016\/j.neucom.2025.129866"},{"issue":"2","key":"5311_CR23","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1093\/jcde\/qwad031","volume":"10","author":"H Kim","year":"2023","unstructured":"Kim, H., Park, C.H., Suh, C., Chae, M., Yoon, H., Youn, B.D.: MPARN: multi-scale path attention residual network for fault diagnosis of rotating machines. J. Comput. Design Eng. 10(2), 860\u2013872 (2023)","journal-title":"J. Comput. Design Eng."},{"key":"5311_CR24","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"5311_CR25","doi-asserted-by":"crossref","unstructured":"Li, J., Wen, Y., He, L.: SCCONV: Spatial and channel reconstruction convolution for feature redundancy. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6153\u20136162 (2023)","DOI":"10.1109\/CVPR52729.2023.00596"},{"key":"5311_CR26","doi-asserted-by":"crossref","unstructured":"Ouali, Y., Hudelot, C., Tami, M.: Spatial contrastive learning for few-shot classification. In: Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, 2021, Proceedings, Part I 21, pp. 671\u2013686 (2021). Springer","DOI":"10.1007\/978-3-030-86486-6_41"},{"key":"5311_CR27","unstructured":"Harris, E., Marcu, A., Painter, M., Niranjan, M., Pr\u00fcgel-Bennett, A., Hare, J.: FMix: Enhancing mixed sample data augmentation. arXiv preprint arXiv:2002.12047 (2020)"},{"key":"5311_CR28","doi-asserted-by":"crossref","unstructured":"Liu, C., Fu, Y., Xu, C., Yang, S., Li, J., Wang, C., Zhang, L.: Learning a few-shot embedding model with contrastive learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 8635\u20138643 (2021)","DOI":"10.1609\/aaai.v35i10.17047"},{"key":"5311_CR29","first-page":"21981","volume":"33","author":"C Doersch","year":"2020","unstructured":"Doersch, C., Gupta, A., Zisserman, A.: Crosstransformers: spatially-aware few-shot transfer. Adv. Neural. Inf. Process. Syst. 33, 21981\u201321993 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5311_CR30","doi-asserted-by":"crossref","unstructured":"Zhao, B., Cui, Q., Song, R., Qiu, Y., Liang, J.: Decoupled knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11953\u201311962 (2022)","DOI":"10.1109\/CVPR52688.2022.01165"},{"key":"5311_CR31","first-page":"719","volume":"31","author":"B Oreshkin","year":"2018","unstructured":"Oreshkin, B., Rodr\u00edguez L\u00f3pez, P., Lacoste, A.: Tadam: Task dependent adaptive metric for improved few-shot learning. Adv. Neural Inf. Process. Syst. 31, 719\u2013729 (2018)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5311_CR32","first-page":"3637","volume":"29","author":"O Vinyals","year":"2016","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. Adv. Neural Inf. Process. Syst. 29, 3637\u20133645 (2016)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5311_CR33","unstructured":"Yu, T., Bagdasaryan, E., Shmatikov, V.: Salvaging federated learning by local adaptation. arXiv preprint arXiv:2002.04758 (2020)"},{"key":"5311_CR34","unstructured":"Hsu, T.-M.H., Qi, H., Brown, M.: Measuring the effects of non-identical data distribution for federated visual classification. arXiv preprint arXiv:1909.06335 (2019)"},{"key":"5311_CR35","first-page":"4080","volume":"30","author":"J Snell","year":"2017","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Adv. Neural Inf. Process. Syst. 30, 4080\u20134090 (2017)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5311_CR36","doi-asserted-by":"crossref","unstructured":"Fan, C., Huang, J.: Federated few-shot learning with adversarial learning. In: 2021 19th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), pp. 1\u20138 (2021). IEEE","DOI":"10.23919\/WiOpt52861.2021.9589192"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05311-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05311-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05311-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T15:35:32Z","timestamp":1761924932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05311-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"references-count":36,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["5311"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05311-2","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2025,9,15]]},"assertion":[{"value":"29 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 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":"Conflict of interest"}}],"article-number":"797"}}