{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T09:51:17Z","timestamp":1770976277309,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698110","type":"print"},{"value":"9789819698127","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-9812-7_15","type":"book-chapter","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T07:26:29Z","timestamp":1753428389000},"page":"177-187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A High-Dimensional Gradient Inversion Attack Based on Feature Distillation in Federated Learning"],"prefix":"10.1007","author":[{"given":"Hongyun","family":"Cai","sequence":"first","affiliation":[]},{"given":"Mingliang","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Zhiqiang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jiaxin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuhang","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121692","volume":"237","author":"S Zhang","year":"2024","unstructured":"Zhang, S., Yang, Y., Chen, C., et al.: Deep learning-based multimodal emotion recognition from audio, visual, and text modalities: a systematic review of recent advancements and future prospects. Expert Syst. Appl. 237, 121692 (2024)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"15_CR2","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1002\/rob.21918","volume":"37","author":"S Grigorescu","year":"2020","unstructured":"Grigorescu, S., Trasnea, B., Cocias, T., et al.: A survey of deep learning techniques for autonomous driving. J. Field Robot. 37(3), 362\u2013386 (2020)","journal-title":"J. Field Robot."},{"key":"15_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106384","volume":"93","author":"AM Ozbayoglu","year":"2020","unstructured":"Ozbayoglu, A.M., Gudelek, M.U., Sezer, O.B.: Deep learning for financial applications: a survey. Appl. Soft Comput. 93, 106384 (2020)","journal-title":"Appl. Soft Comput."},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Huang, W., Ye, M., Shi, Z., et al.: Federated learning for generalization, robustness, fairness: a survey and benchmark. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3418862"},{"issue":"5","key":"15_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3637868","volume":"56","author":"M Gecer","year":"2024","unstructured":"Gecer, M., Garbinato, B.: Federated learning for mobility applications. ACM Comput. Surv. 56(5), 1\u201328 (2024)","journal-title":"ACM Comput. Surv."},{"key":"15_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102796","volume":"116","author":"D Wang","year":"2025","unstructured":"Wang, D., Guan, S.: FedFR-ADP: adaptive differential privacy with feedback regulation for robust model performance in federated learning. Inf. Fusion 116, 102796 (2025)","journal-title":"Inf. Fusion"},{"issue":"7","key":"15_CR7","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1109\/TMI.2023.3239391","volume":"42","author":"A Hatamizadeh","year":"2023","unstructured":"Hatamizadeh, A., Yin, H., Molchanov, P., et al.: Do gradient inversion attacks make federated learning unsafe? IEEE Trans. Med. Imaging 42(7), 2044\u20132056 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Zhu, L., Liu, Z., Han, S.: Deep leakage from gradients. In: NeurIPS, (2019)","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"15_CR9","unstructured":"Zhao, B., Mopuri, K. R., Bilen, H.: iDLG: improved deep leakage from gradients (2020). arXiv preprint arXiv:2001.02610"},{"key":"15_CR10","first-page":"29898","volume":"34","author":"J Jeon","year":"2021","unstructured":"Jeon, J., Lee, K., Oh, S., et al.: Gradient inversion with generative image prior. Adv. Neural Inf. Process. Syst. 34, 29898\u201329908 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1109\/TIFS.2022.3227761","volume":"18","author":"H Yang","year":"2022","unstructured":"Yang, H., Ge, M., Xiang, K., et al.: Using highly compressed gradients in federated learning for data reconstruction attacks. IEEE Trans. Inf. Forensics Secur. 18, 818\u2013830 (2022)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"15_CR12","unstructured":"Yang, Z., Zhang, Y., Zheng, Y., et al.: FedFed: feature distillation against data heterogeneity in federated learning. Adv. Neural Inf. Process. Syst. (2024)"},{"key":"15_CR13","unstructured":"Kim, S., Jeong, M., Kim, S., et al.: FedDr+: stabilizing dot-regression with global feature distillation for federated learning (2024). arXiv preprint arXiv:2406.02355"},{"key":"15_CR14","unstructured":"Yue, K., Wong, C.W., et al.: Gradient obfuscation gives a false sense of security in federated learning. In: 32nd USENIX Security Symposium (USENIX Security 23), pp. 6381\u20136398 (2023)"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., et al.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"issue":"2","key":"15_CR16","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C.C., He, K., et al.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"15_CR17","unstructured":"LeCun, Y.: The MNIST database of handwritten digits. (5, 6). http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"15_CR18","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. Tech. Rep. Citeseer (5, 6) (2009)"},{"key":"15_CR19","unstructured":"Yin, Z., Xing, E., Shen, Z.: Squeeze, recover and relabel: dataset condensation at imagenet scale from a new perspective. In: Adv. Neural Inf. Process. Syst. (2024)"},{"issue":"11","key":"15_CR20","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., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y., Xu, K., Hancke, G.P., et al.: Color shift estimation-and-correction for image enhancement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 25389\u201325398 (2024)","DOI":"10.1109\/CVPR52733.2024.02399"},{"issue":"4","key":"15_CR22","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9812-7_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T08:57:16Z","timestamp":1770973036000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9812-7_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698110","9789819698127"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9812-7_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","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":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}