{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T03:29:35Z","timestamp":1777865375579,"version":"3.51.4"},"reference-count":43,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100010877","name":"Science, Technology and Innovation Commission of Shenzhen Municipality","doi-asserted-by":"publisher","award":["GJHZ20240218114659027"],"award-info":[{"award-number":["GJHZ20240218114659027"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["YCJJ20252336"],"award-info":[{"award-number":["YCJJ20252336"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,19]]},"DOI":"10.1109\/iccv51701.2025.00373","type":"proceedings-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:45:49Z","timestamp":1777491949000},"page":"3916-3925","source":"Crossref","is-referenced-by-count":0,"title":["FLSeg: Enhancing Privacy and Robustness in Federated Learning under Heterogeneous Data via Model Segmentation"],"prefix":"10.1109","author":[{"given":"Zichun","family":"Su","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renfei","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songfeng","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3214303"},{"key":"ref2","first-page":"4805","article-title":"ACORN: input validation for secure aggregation","volume-title":"32nd USENIX Security Symposium, USENIX Security 2023","author":"Bell","year":"2023"},{"key":"ref3","first-page":"1253","article-title":"Secure singleserver aggregation with (poly)logarithmic overhead","volume-title":"CCS \u201920: 2020 ACM SIGSAC Conference on Computer and Communications Security","author":"Henry Bell","year":"2020"},{"key":"ref4","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume":"30","author":"Blanchard","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref6","article-title":"Leaf: A benchmark for federated settings","author":"Caldas","year":"2018","journal-title":"arXiv preprint"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.2001.959888"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"},{"key":"ref9","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"NeurIPS","author":"Fallah","year":"2020"},{"key":"ref10","first-page":"1605","article-title":"Local model poisoning attacks to \\{Byzantine-Robust\\} federated learning","volume-title":"29th USENIX security symposium (USENIX Security 20)","author":"Fang","year":"2020"},{"key":"ref11","first-page":"1605","article-title":"Local model poisoning attacks to byzantinerobust federated learning","volume-title":"USENIX Security Symposium","author":"Fang","year":"2020"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3998\/mpub.11978139.cmp.4"},{"key":"ref13","first-page":"16937","article-title":"Inverting gradients-how easy is it to break privacy in federated learning?","volume":"33","author":"Geiping","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref14","first-page":"218","article-title":"How to play any mental game or A completeness theorem for protocols with honest majority","volume-title":"STOC","author":"Goldreich","year":"1987"},{"key":"ref15","first-page":"3521","article-title":"The hidden vulnerability of distributed learning in byzantium","volume-title":"International Conference on Machine Learning","author":"Guerraoui","year":"2018"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3467956"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3212627"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref19","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Karimireddy","year":"2020"},{"key":"ref20","article-title":"Byzantine-robust learning on heterogeneous datasets via bucketing","volume-title":"The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event","author":"Karimireddy","year":"2022"},{"key":"ref21","volume-title":"Learning multiple layers of features from tiny images.","author":"Krizhevsky","year":"2009"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2023.3237397"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/IoTDI61053.2024.00018"},{"key":"ref24","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through per-sonalization","volume-title":"International conference on machine learning","author":"Li","year":"2021"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3108434"},{"key":"ref26","first-page":"21404","article-title":"Byzantine-robust learning on heterogeneous data via gradient splitting","volume-title":"International Conference on Machine Learning","author":"Liu","year":"2023"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3402993"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179400"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3169918"},{"key":"ref30","first-page":"1273","article-title":"Communicationefficient learning of deep networks from decentralized data","volume-title":"Artificial intelligence and statistics","author":"McMahan","year":"2017"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3501296"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3153135"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24498"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833647"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/217"},{"key":"ref36","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume":"33","author":"Dinh","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3056991"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3331274"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TMLCN.2023.3344074"},{"key":"ref40","first-page":"261","article-title":"Fall of empires: Breaking byzantine-tolerant sgd by inner product manipulation","volume-title":"Uncertainty in Artificial Intelligence","author":"Xie","year":"2020"},{"key":"ref41","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"International Conference on Machine Learning","author":"Yin","year":"2018"},{"key":"ref42","article-title":"Idlg: Improved deep leakage from gradients","volume":"abs\/2001.02610","author":"Zhao","year":"2020","journal-title":"CoRR"},{"key":"ref43","article-title":"Deep leakage from gradients","volume":"32","author":"Zhu","year":"2019","journal-title":"Advances in neural information processing systems"}],"event":{"name":"2025 IEEE\/CVF International Conference on Computer Vision (ICCV)","location":"Honolulu, HI, USA","start":{"date-parts":[[2025,10,19]]},"end":{"date-parts":[[2025,10,25]]}},"container-title":["2025 IEEE\/CVF International Conference on Computer Vision (ICCV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11443115\/11443287\/11445230.pdf?arnumber=11445230","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T06:22:06Z","timestamp":1777530126000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11445230\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/iccv51701.2025.00373","relation":{},"subject":[],"published":{"date-parts":[[2025,10,19]]}}}