{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T07:10:16Z","timestamp":1737443416779,"version":"3.33.0"},"reference-count":45,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T00:00:00Z","timestamp":1728172800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T00:00:00Z","timestamp":1728172800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,6]]},"DOI":"10.1109\/smc54092.2024.10831729","type":"proceedings-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T18:39:20Z","timestamp":1737398360000},"page":"1530-1537","source":"Crossref","is-referenced-by-count":0,"title":["Virtual Feature Generation and Learning for Federated Domain Generalization"],"prefix":"10.1109","author":[{"given":"WenKang","family":"Kong","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University,Beijing,China,100044"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Huang","sequence":"additional","affiliation":[{"name":"Network Information Center, South China Normal University,Guangzhou,China,510006"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ZhuoMing","family":"Liang","sequence":"additional","affiliation":[{"name":"Network Information Center, South China Normal University,Guangzhou,China,510006"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Fed-grab: Federated long-tailed learning with self-adjusting gradient balancer","volume":"36","author":"Xiao","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1109\/ICRA48506.2021.9561612"},{"key":"ref3","article-title":"A survey of large language models","author":"Zhao","year":"2023","journal-title":"arXiv preprint"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.5555\/2999134.2999257"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/TKDE.2022.3178128"},{"volume-title":"European Parliament and Council of the European Union. Regulation (EU) 2016\/679 of the European Parliament and of the Council","key":"ref6"},{"year":"2018","journal-title":"Cal. Civ. Code \u00a7 1798.100 et seq.","article-title":"California consumer privacy act of 2018","key":"ref7"},{"volume-title":"Passed at the 30th meeting of the Standing Committee of the 13th National People\u2019s Congress on August 20, 2021, Effective Nov. 1, 2021","year":"2021","article-title":"Personal information protection law of the people\u2019s republic of china","key":"ref8"},{"key":"ref9","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentral-ized data","author":"McMahan","year":"2017","journal-title":"Artificial intelligence and statistics. PMLR"},{"key":"ref10","article-title":"Federated learning with non-iid data","author":"Zhao","year":"2018","journal-title":"arXiv preprint"},{"key":"ref11","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of Machine learning and systems","volume":"2","author":"Li"},{"key":"ref12","article-title":"Xor mixup: Privacy-preserving data augmentation for one-shot federated learning","author":"Shin","year":"2020","journal-title":"arXiv preprint"},{"key":"ref13","article-title":"Fedmix: Approximation of mixup under mean augmented federated learning","author":"Yoon","year":"2021","journal-title":"arXiv preprint"},{"key":"ref14","article-title":"Differentially private learning needs better features (or much more data)","author":"Tramer","year":"2020","journal-title":"arXiv preprint"},{"key":"ref15","article-title":"Communication-efficient on-device machine learning: Federated dis-tillation and augmentation under non-iid private data","author":"Jeong","year":"2018","journal-title":"arXiv preprint"},{"key":"ref16","first-page":"111","article-title":"Virtual ho-mogeneity learning: Defending against data heterogeneity in federated learning","volume-title":"International Conference on Machine Learning","volume":"21","author":"Tang"},{"key":"ref17","volume-title":"Continuous multivariate distributions, Volume 1: Models and applications","volume":"334","author":"Kotz","year":"2019"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"ref19","first-page":"361","article-title":"Federated domain gen-eralization for image recognition via cross-client style transfer","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Chen"},{"key":"ref20","article-title":"What do we mean by generalization in federated learning?","author":"Yuan","year":"2021","journal-title":"arXiv preprint"},{"key":"ref21","first-page":"5","article-title":"Kd3a: Unsupervised multi-source decentralized domain adaptation via knowledge distillation","volume":"4","author":"Feng","year":"2021","journal-title":"ICML"},{"key":"ref22","article-title":"Interpret federated learning with shapley values","author":"Wang","year":"2019","journal-title":"arXiv preprint"},{"key":"ref23","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of Machine learning and systems","volume":"2","author":"Li"},{"key":"ref24","article-title":"Overcoming forgetting in federated learning on non-iid data","author":"Shoham","year":"2019","journal-title":"arXiv preprint"},{"key":"ref25","first-page":"21 394","article-title":"Personalized federated learning with moreau envelopes","volume":"33","author":"Dinh","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref26","article-title":"Federated learning based on dynamic regularization","author":"Acar","year":"2021","journal-title":"arXiv preprint"},{"author":"Fort","journal-title":"arXiv preprint","article-title":"Stiffness: A new perspective on generalization in neural networks","key":"ref27"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.1109\/CVPR46437.2021.01057"},{"doi-asserted-by":"publisher","key":"ref29","DOI":"10.1109\/ICCV48922.2021.00438"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.1609\/aaai.v36i8.20819"},{"doi-asserted-by":"publisher","key":"ref31","DOI":"10.1016\/j.future.2023.01.019"},{"doi-asserted-by":"publisher","key":"ref32","DOI":"10.1109\/ICDCS.2019.00099"},{"doi-asserted-by":"publisher","key":"ref33","DOI":"10.1016\/j.sysarc.2021.102125"},{"issue":"5","key":"ref34","first-page":"1","article-title":"Auto-weighted robust federated learning with corrupted data sources","volume":"13","author":"Li","year":"2022","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"ref35","first-page":"38 831","article-title":"Fedsr: A simple and effective domain generalization method for federated learning","volume":"35","author":"Nguyen","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref36","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"International conference on machine learning","author":"Ganin"},{"key":"ref37","article-title":"Federated learning with domain generalization","author":"Zhang","year":"2021","journal-title":"arXiv preprint"},{"key":"ref38","article-title":"Federated adversarial domain adaptation","author":"Peng","year":"2019","journal-title":"arXiv preprint"},{"key":"ref39","first-page":"942","article-title":"Feature distribution matching for federated domain generalization","volume-title":"Asian Conference on Machine Learning","author":"Sun"},{"key":"ref40","article-title":"Interpretable complex-valued neural networks for privacy protection","author":"Xiang","year":"2019","journal-title":"arXiv preprint"},{"key":"ref41","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"International conference on artificial intelligence and statistics","author":"Bagdasaryan"},{"doi-asserted-by":"publisher","key":"ref42","DOI":"10.1109\/TPAMI.2022.3195549"},{"key":"ref43","first-page":"1","article-title":"Adversarial target-invariant representation learning for domain generalization","volume":"8","author":"Albuquerque","journal-title":"arXiv preprint"},{"doi-asserted-by":"publisher","key":"ref44","DOI":"10.1145\/3605573.3605726"},{"key":"ref45","article-title":"Deep domain confusion: Maximizing for domain invariance","author":"Tzeng","year":"2014","journal-title":"arXiv preprint"}],"event":{"name":"2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","start":{"date-parts":[[2024,10,6]]},"location":"Kuching, Malaysia","end":{"date-parts":[[2024,10,10]]}},"container-title":["2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10830919\/10830920\/10831729.pdf?arnumber=10831729","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T06:32:57Z","timestamp":1737441177000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10831729\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,6]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/smc54092.2024.10831729","relation":{},"subject":[],"published":{"date-parts":[[2024,10,6]]}}}