{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T19:58:43Z","timestamp":1768593523220,"version":"3.49.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T00:00:00Z","timestamp":1650585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T00:00:00Z","timestamp":1650585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s00530-022-00927-5","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T06:03:10Z","timestamp":1650607390000},"page":"1833-1843","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["FedFV: federated face verification via equivalent class embeddings"],"prefix":"10.1007","volume":"28","author":[{"given":"Lingyun","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Haoyuan","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Xingtao","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,22]]},"reference":[{"key":"927_CR1","doi-asserted-by":"crossref","unstructured":"O\u2019Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Hernandez, G.V., Krpalkova, L., Riordan, D., Walsh, J.: Deep learning vs. traditional computer vision. In: Science and Information Conference, pp. 128\u2013144 (2019). Springer","DOI":"10.1007\/978-3-030-17795-9_10"},{"key":"927_CR2","doi-asserted-by":"crossref","unstructured":"Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 2018 (2018)","DOI":"10.1155\/2018\/7068349"},{"issue":"2","key":"927_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3042064","volume":"50","author":"A Ioannidou","year":"2017","unstructured":"Ioannidou, A., Chatzilari, E., Nikolopoulos, S., Kompatsiaris, I.: Deep learning advances in computer vision with 3d data: a survey. ACM Comput. Surv. (CSUR) 50(2), 1\u201338 (2017)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"11","key":"927_CR4","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"927_CR5","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"927_CR6","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition (2015)","DOI":"10.5244\/C.29.41"},{"key":"927_CR7","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: Closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)","DOI":"10.1109\/CVPR.2014.220"},{"key":"927_CR8","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891\u20131898 (2014)","DOI":"10.1109\/CVPR.2014.244"},{"key":"927_CR9","unstructured":"Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: NIPS, pp. 1988\u20131996 (2014). https:\/\/linproxy.fan.workers.dev:443\/http\/papers.nips.cc\/paper\/5416-deep-learning-face-representation-by-joint-identification-verification"},{"key":"927_CR10","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892\u20132900 (2015)","DOI":"10.1109\/CVPR.2015.7298907"},{"key":"927_CR11","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"927_CR12","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: European Conference on Computer Vision, pp. 499\u2013515 (2016). Springer","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"927_CR13","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: Deep hypersphere embedding for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.713"},{"key":"927_CR14","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., Liu, W.: Cosface: Large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00552"},{"key":"927_CR15","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"927_CR16","doi-asserted-by":"crossref","unstructured":"Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D\u2019Oliveira, R.G.L., Eichner, H., Rouayheb, S.E., Evans, D., Gardner, J., Garrett, Z., Gasc\u00f3n, A., Ghazi, B., Gibbons, P.B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Kone\u010dn\u00fd, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., \u00d6zg\u00fcr, A., Pagh, R., Raykova, M., Qi, H., Ramage, D., Raskar, R., Song, D., Song, W., sTICH, S.U., Sun, Z., Suresh, A.T., Tram\u00e8r, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F.X., Yu, H., Zhao, S.: Advances and Open Problems in Federated Learning (2021)","DOI":"10.1561\/9781680837896"},{"issue":"2","key":"927_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1\u201319 (2019)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"927_CR18","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Singh, A., Zhu, J. (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 54, pp. 1273\u20131282 (2017). PMLR. https:\/\/linproxy.fan.workers.dev:443\/https\/proceedings.mlr.press\/v54\/mcmahan17a.html"},{"issue":"3","key":"927_CR19","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37(3), 50\u201360 (2020). https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1109\/MSP.2020.2975749","journal-title":"IEEE Signal Process. Mag."},{"key":"927_CR20","unstructured":"Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Kone\u010dn\u00fd, J., Mazzocchi, S., McMahan, H.B., Overveldt, T.V., Petrou, D., Ramage, D., Roselander, J.: Towards Federated Learning at Scale: System Design (2019)"},{"key":"927_CR21","unstructured":"Kone\u010dn\u00fd, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., Bacon, D.: Federated Learning: Strategies for Improving Communication Efficiency (2017)"},{"key":"927_CR22","unstructured":"Hsu, T.-M.H., Qi, H., Brown, M.: Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification (2019)"},{"key":"927_CR23","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: Scaffold: Stochastic controlled averaging for federated learning. In: International Conference on Machine Learning, pp. 5132\u20135143 (2020). PMLR"},{"key":"927_CR24","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated Optimization in Heterogeneous Networks (2020)"},{"key":"927_CR25","unstructured":"Wang, J., Liu, Q., Liang, H., Joshi, G., Poor, H.V.: Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization (2020)"},{"key":"927_CR26","unstructured":"Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Kone\u010dn\u00fd, J., Kumar, S., McMahan, H.B.: Adaptive Federated Optimization (2021)"},{"key":"927_CR27","unstructured":"Hosseini, H., Park, H., Yun, S., Louizos, C., Soriaga, J., Welling, M.: Federated Learning of User Verification Models Without Sharing Embeddings (2021)"},{"key":"927_CR28","unstructured":"Bojanowski, P., Joulin, A.: Unsupervised learning by predicting noise. In: International Conference on Machine Learning, pp. 517\u2013526 (2017). PMLR"},{"key":"927_CR29","unstructured":"Yu, F., Rawat, A.S., Menon, A., Kumar, S.: Federated learning with only positive labels. In: International Conference on Machine Learning, pp. 10946\u201310956 (2020). PMLR. https:\/\/linproxy.fan.workers.dev:443\/https\/proceedings.mlr.press\/v119\/yu20f.html"},{"key":"927_CR30","unstructured":"Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Learned-Miller, E., Ferencz, A., Jurie, F. (eds) Workshop on Faces in \u2019Real-Life\u2019 Images: Detection, Alignment, and Recognition, Marseille, France (2008). https:\/\/linproxy.fan.workers.dev:443\/https\/hal.inria.fr\/inria-00321923"},{"key":"927_CR31","doi-asserted-by":"crossref","unstructured":"Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: Agedb: The first manually collected, in-the-wild age database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2017)","DOI":"10.1109\/CVPRW.2017.250"},{"key":"927_CR32","doi-asserted-by":"publisher","unstructured":"Sengupta, S., Chen, J.-C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W.: Frontal to profile face verification in the wild. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1\u20139 (2016). https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1109\/WACV.2016.7477558","DOI":"10.1109\/WACV.2016.7477558"},{"key":"927_CR33","unstructured":"Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: ICML, vol. 2, p. 7 (2016)"},{"key":"927_CR34","doi-asserted-by":"publisher","unstructured":"Wang, F., Xiang, X., Cheng, J., Yuille, A.L.: Normface: L2 hypersphere embedding for face verification. In: Proceedings of the 25th ACM International Conference on Multimedia. MM \u201917, pp. 1041\u20131049. Association for Computing Machinery, New York, NY, USA (2017). https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1145\/3123266.3123359","DOI":"10.1145\/3123266.3123359"},{"issue":"7","key":"927_CR35","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1109\/LSP.2018.2822810","volume":"25","author":"F Wang","year":"2018","unstructured":"Wang, F., Cheng, J., Liu, W., Liu, H.: Additive margin softmax for face verification. IEEE Signal Process. Lett. 25(7), 926\u2013930 (2018). https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1109\/LSP.2018.2822810","journal-title":"IEEE Signal Process. Lett."},{"key":"927_CR36","unstructured":"Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., Ramage, D.: Federated Learning for Mobile Keyboard Prediction (2019)"},{"key":"927_CR37","doi-asserted-by":"crossref","unstructured":"Duong, C.N., Truong, T.-D., Luu, K., Quach, K.G., Bui, H., Roy, K.: Vec2face: Unveil human faces from their blackbox features in face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6132\u20136141 (2020)","DOI":"10.1109\/CVPR42600.2020.00617"},{"key":"927_CR38","unstructured":"Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning Face Representation from Scratch (2014)"},{"key":"927_CR39","doi-asserted-by":"crossref","unstructured":"Wu, Y., He, K.: Group normalization. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01261-8_1"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s00530-022-00927-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/article\/10.1007\/s00530-022-00927-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s00530-022-00927-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T12:25:44Z","timestamp":1664108744000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/link.springer.com\/10.1007\/s00530-022-00927-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,22]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["927"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1007\/s00530-022-00927-5","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,22]]},"assertion":[{"value":"17 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2022","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 have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}