{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T07:11:18Z","timestamp":1784099478897,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819227587","type":"print"},{"value":"9789819227594","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-2759-4_43","type":"book-chapter","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T06:51:23Z","timestamp":1784098283000},"page":"589-604","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Variational Bayesian Gradient Inversion Attack with\u00a0Diffusion Models in\u00a0Federated Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6037-3760","authenticated-orcid":false,"given":"Linwei","family":"Fang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"43_CR1","unstructured":"Balunovic, M., Dimitrov, D.I., Staab, R., Vechev, M.: Bayesian framework for gradient leakage. In: International Conference on Learning Representations (2022)"},{"key":"43_CR2","unstructured":"Chung, H., Kim, J., Mccann, M.T., Klasky, M.L., Ye, J.C.: Diffusion posterior sampling for general noisy inverse problems. arXiv preprint arXiv:2209.14687 (2022)"},{"key":"43_CR3","doi-asserted-by":"publisher","first-page":"25683","DOI":"10.52202\/068431-1862","volume":"35","author":"H Chung","year":"2022","unstructured":"Chung, H., Sim, B., Ryu, D., Ye, J.C.: Improving diffusion models for inverse problems using manifold constraints. Adv. Neural. Inf. Process. Syst. 35, 25683\u201325696 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"43_CR4","unstructured":"Daras, G., et al.: A survey on diffusion models for inverse problems. arXiv preprint arXiv:2410.00083 (2024)"},{"issue":"496","key":"43_CR5","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.1198\/jasa.2011.tm11181","volume":"106","author":"B Efron","year":"2011","unstructured":"Efron, B.: Tweedie\u2019s formula and selection bias. J. Am. Stat. Assoc. 106(496), 1602\u20131614 (2011)","journal-title":"J. Am. Stat. Assoc."},{"key":"43_CR6","doi-asserted-by":"crossref","unstructured":"Fang, H., Chen, B., Wang, X., Wang, Z., Xia, S.T.: Gifd: a generative gradient inversion method with feature domain optimization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4967\u20134976 (2023)","DOI":"10.1109\/ICCV51070.2023.00458"},{"key":"43_CR7","first-page":"16937","volume":"33","author":"J Geiping","year":"2020","unstructured":"Geiping, J., Bauermeister, H., Dr\u00f6ge, H., Moeller, M.: Inverting gradients-how easy is it to break privacy in federated learning? Adv. Neural. Inf. Process. Syst. 33, 16937\u201316947 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"6","key":"43_CR8","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1109\/TBDATA.2023.3239116","volume":"10","author":"J Geng","year":"2023","unstructured":"Geng, J., Mou, Y., Li, Q., Li, F., Beyan, O., Decker, S., Rong, C.: Improved gradient inversion attacks and defenses in federated learning. IEEE Trans. Big Data 10(6), 839\u2013850 (2023)","journal-title":"IEEE Trans. Big Data"},{"key":"43_CR9","unstructured":"Geyer, R.C., Klein, T., Nabi, M.: Differentially private federated learning: a client level perspective. arXiv preprint arXiv:1712.07557 (2017)"},{"key":"43_CR10","doi-asserted-by":"publisher","first-page":"14715","DOI":"10.52202\/068431-1070","volume":"35","author":"A Graikos","year":"2022","unstructured":"Graikos, A., Malkin, N., Jojic, N., Samaras, D.: Diffusion models as plug-and-play priors. Adv. Neural. Inf. Process. Syst. 35, 14715\u201314728 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"43_CR11","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"43_CR12","first-page":"29898","volume":"34","author":"J Jeon","year":"2021","unstructured":"Jeon, J., Lee, K., Oh, S., Ok, J., 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":"43_CR13","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhang, J., Liu, L., Liu, J.: Auditing privacy defenses in federated learning via generative gradient leakage. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10132\u201310142 (2022)","DOI":"10.1109\/CVPR52688.2022.00989"},{"key":"43_CR14","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"43_CR15","unstructured":"Minka, T., et\u00a0al.: Divergence measures and message passing. Microsoft Research Technical Report (2005)"},{"key":"43_CR16","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, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"issue":"2","key":"43_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":"43_CR18","doi-asserted-by":"crossref","unstructured":"Ye, Z., Luo, W., Zhou, Q., Tang, Y.: High-fidelity gradient inversion in distributed learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 19983\u201319991 (2024)","DOI":"10.1609\/aaai.v38i18.29975"},{"key":"43_CR19","doi-asserted-by":"crossref","unstructured":"Yin, H., Mallya, A., Vahdat, A., Alvarez, J.M., Kautz, J., Molchanov, P.: See through gradients: Image batch recovery via gradinversion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16337\u201316346 (2021)","DOI":"10.1109\/CVPR46437.2021.01607"},{"issue":"10","key":"43_CR20","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499\u20131503 (2016)","journal-title":"IEEE Signal Process. Lett."},{"key":"43_CR21","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: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"43_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, R., Guo, S., Wang, J., Xie, X., Tao, D.: A survey on gradient inversion: Attacks, defenses and future directions. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, pp. 5678\u2013685 (2023)","DOI":"10.24963\/ijcai.2022\/791"},{"key":"43_CR23","unstructured":"Zhu, L., Liu, Z., Han, S.: Deep leakage from gradients. Advances in neural information processing systems 32 (2019)"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-2759-4_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T06:51:33Z","timestamp":1784098293000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-2759-4_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"ISBN":["9789819227587","9789819227594"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-2759-4_43","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,16]]},"assertion":[{"value":"16 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ksem2026.rosc.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}