{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T03:16:34Z","timestamp":1769742994491,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556953","type":"print"},{"value":"9789819556960","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-5696-0_20","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:03:17Z","timestamp":1769695397000},"page":"281-294","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Enhanced Federated Prototype Learning Method Under Domain Shift"],"prefix":"10.1007","author":[{"given":"Liang","family":"Kuang","sequence":"first","affiliation":[]},{"given":"Kuangpu","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Jianguo","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"20_CR1","unstructured":"Acar, D.A.E., Zhao, Y., Navarro, R.M., Mattina, M., Whatmough, P. N., Saligrama, V.: Federated learning based on dynamic regularization. In: Proceedings of the ICLR (2021)"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Dai, Y., Chen, Z., Li, J., Heinecke, S., Sun, L., Xu, R.: Tackling data heterogeneity in federated learning with class prototypes. In: Proceedings of the AAAI, pp. 7314\u20137322 (2023)","DOI":"10.1609\/aaai.v37i6.25891"},{"key":"20_CR3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","volume":"134","author":"PT De Boer","year":"2005","unstructured":"De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134, 19\u201367 (2005). https:\/\/doi.org\/10.1007\/s10479-005-5724-z","journal-title":"Ann. Oper. Res."},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: Proceedings of the CVPR, pp. 2066\u20132073 (2012)","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Guo, K., Ding, Y., Liang, J., He, R., Wang, Z., Tan, T.: Exploring vacant classes in label-skewed federated learning. In: Proceedings of the AAAI, pp. 16960\u201316968 (2025)","DOI":"10.1609\/aaai.v39i16.33864"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Guo, Y., et al.: HCSC: hierarchical contrastive selective coding. In: Proceedings of the CVPR, pp. 9706\u20139715 (2022)","DOI":"10.1109\/CVPR52688.2022.00948"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"20_CR8","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TPAMI.2023.3327373","volume":"46","author":"W Huang","year":"2024","unstructured":"Huang, W., Ye, M., Shi, Z., Du, B.: Generalizable heterogeneous federated cross-correlation and instance similarity learning. IEEE Trans. Pattern Anal. Mach. Intell. 46, 712\u2013728 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Huang, W., Ye, M., Shi, Z., Li, H., Du, B.: Rethinking federated learning with domain shift: a prototype view. In: Proceedings of the CVPR, pp. 16312\u201316322 (2023)","DOI":"10.1109\/CVPR52729.2023.01565"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Jetley, S., Romera-Paredes, B., Jayasumana, S., Torr, P.H.S.: Prototypical priors: from improving classification to zero-shot learning. In: Proceedings of the BMVC, pp. 120.1\u2013120.12 (2015)","DOI":"10.5244\/C.29.120"},{"key":"20_CR11","unstructured":"Kang, Z., Grauman, K., Sha, F.: Learning with whom to share in multi-task feature learning. In: Proceedings of the ICML, pp. 521\u2013528 (2011)"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: Proceedings of the CVPR, pp. 10713\u201310722 (2021)","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"20_CR13","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. In: Proceedings of the MLSys, pp. 429\u2013450 (2020)"},{"key":"20_CR14","unstructured":"Luo, M., Chen, F., Hu, D., Zhang, Y., Liang, J., Feng, J.: No fear of heterogeneity: classifier calibration for federated learning with non-IID data. In: Proceedings of the NeurIPS, pp. 5972\u20135984 (2021)"},{"key":"20_CR15","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.Y.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the AISTATS, pp. 1273\u20131282 (2017)"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Mendieta, M., Yang, T., Wang, P., Lee, M., Ding, Z., Chen, C.: Local learning matters: rethinking data heterogeneity in federated learning. In: Proceedings of the CVPR, pp. 8397\u20138406 (2022)","DOI":"10.1109\/CVPR52688.2022.00821"},{"key":"20_CR17","unstructured":"Mettes, P., Van\u00a0der Pol, E., Snoek, C.: Hyperspherical prototype networks. In: Proceedings of the NeurIPS, pp. 1476\u20131486 (2019)"},{"key":"20_CR18","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.future.2023.01.019","volume":"143","author":"X Mu","year":"2023","unstructured":"Mu, X., et al.: FedProc: prototypical contrastive federated learning on non-IID data. Fut. Gener. Comput. Syst. 143, 93\u2013104 (2023)","journal-title":"Fut. Gener. Comput. Syst."},{"key":"20_CR19","unstructured":"Jaehoon, O., Kim, S., Yun, S. Y.: FedBABU: towards enhanced representation for federated image classification. In: Proceedings of the ICLR (2022)"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: Proceedings of the ICCV, pp. 1406\u20131415 (2019)","DOI":"10.1109\/ICCV.2019.00149"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Sarfraz, S., Sharma, V., Stiefelhagen, R.: Efficient parameter-free clustering using first neighbor relations. In: Proceedings of the CVPR, pp. 8934\u20138943 (2019)","DOI":"10.1109\/CVPR.2019.00914"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Shang, X., Lu, Y., Huang, G., Wang, H.: Federated learning on heterogeneous and long-tailed data via classifier re-training with federated features. In: Proceedings of the IJCAI, pp. 2218\u20132224 (2022)","DOI":"10.24963\/ijcai.2022\/308"},{"key":"20_CR23","unstructured":"Shi, Y., Liang, J., Zhang, W., Tan, V.Y.F., Bai, S.: Towards understanding and mitigating dimensional collapse in heterogeneous federated learning. In: Proceedings of the ICLR (2023)"},{"issue":"5","key":"20_CR24","doi-asserted-by":"publisher","first-page":"2936","DOI":"10.1109\/TPAMI.2023.3338063","volume":"46","author":"Y Shi","year":"2024","unstructured":"Shi, Y., Liang, J., Zhang, W., Xue, C., Tan, V.Y.F., Bai, S.: Understanding and mitigating dimensional collapse in federated learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(5), 2936\u20132949 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR25","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Proceedings of the NeurIPS, pp. 4077\u20134087, (2017)"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Tan, Y., et al.: FedProto: Federated prototype learning across heterogeneous clients. In: Proceedings of the AAAI, pp. 8432\u20138440 (2022)","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"20_CR27","unstructured":"Tan, Y., Long, G., Ma, J., Liu, L., Zhou, T., Jiang, J.: Federated learning from pre-trained models: a contrastive learning approach. In: Proceedings of the NeurIPS, pp. 19332\u201319344 (2022)"},{"key":"20_CR28","unstructured":"Tanwisuth, K.: A prototype-oriented framework for unsupervised domain adaptation. In: Proceedings of the NeurIPS, pp. 17194\u201317208 (2021)"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. In: Proceedings of the CVPR, pp. 2495\u20132504 (2021)","DOI":"10.1109\/CVPR46437.2021.00252"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Wang, L., Bian, J., Zhang, L., Chen, C., Xu, J.: Taming cross-domain representation variance in federated prototype learning with heterogeneous data domains. In: Proceedings of the NeurIPS, pp. 88348\u201388372 (2024)","DOI":"10.52202\/079017-2803"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, K., Lu, Z., Wang, Y., Liang, J.: Personalized federated learning via dual-prompt optimization and cross fusion (2025). arXiv preprint arXiv:2506.21144,","DOI":"10.2139\/ssrn.6056163"},{"key":"20_CR32","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-IID data. arXiv preprint arXiv:1806.00582 (2018)"},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, Y., Hospedales, T., Xiang, T.: Learning to generate novel domains for domain generalization. In: Proceedings of the ECCV, pp. 561\u2013578 (2020)","DOI":"10.1007\/978-3-030-58517-4_33"},{"key":"20_CR34","unstructured":"Zhou, T., Zhang, J., Tsang, D.\u00a0H.: FedFA: federated learning with feature anchors to align features and classifiers for heterogeneous data. IEEE Trans. Mob. Comput., 4811\u20134824 (2023)"},{"key":"20_CR35","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2021.07.098","volume":"465","author":"H Zhu","year":"2021","unstructured":"Zhu, H., Jinjin, X., Liu, S., Jin, Y.: Federated learning on non-IID data: a survey. Neurocomputing 465, 371\u2013390 (2021)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5696-0_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:03:30Z","timestamp":1769695410000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5696-0_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556953","9789819556960"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5696-0_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}