{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:41:01Z","timestamp":1775144461690,"version":"3.50.1"},"reference-count":74,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Project of China","award":["2019YFB1312000"],"award-info":[{"award-number":["2019YFB1312000"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076195"],"award-info":[{"award-number":["62076195"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["AUGA5710011522"],"award-info":[{"award-number":["AUGA5710011522"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1109\/tnnls.2022.3214573","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T01:04:20Z","timestamp":1667523860000},"page":"7190-7203","source":"Crossref","is-referenced-by-count":11,"title":["Deep Class-Incremental Learning From Decentralized Data"],"prefix":"10.1109","volume":"35","author":[{"given":"Xiaohan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7854-3417","authenticated-orcid":false,"given":"Songlin","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}]},{"given":"Jinjie","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Software Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7252-5047","authenticated-orcid":false,"given":"Qi","family":"Tian","sequence":"additional","affiliation":[{"name":"Cloud BU, Huawei, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1793-5836","authenticated-orcid":false,"given":"Yihong","family":"Gong","sequence":"additional","affiliation":[{"name":"College of Software Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0611-0636","authenticated-orcid":false,"given":"Xiaopeng","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref3","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"25","author":"Krizhevsky"},{"key":"ref4","first-page":"1","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Ren"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.713"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2973812"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3102955"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/s0079-7421(08)60536-8"},{"key":"ref9","first-page":"409","article-title":"Incremental and decremental support vector machine learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cauwenberghs"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.431"},{"key":"ref11","first-page":"1","article-title":"Overcoming catastrophic forgetting by incremental moment matching","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lee"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01220"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475265"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58574-7_42"},{"key":"ref15","article-title":"MgSvF: Multi-grained slow vs. fast framework for few-shot class-incremental learning","author":"Zhao","year":"2020","journal-title":"arXiv:2006.15524"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.01.012"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6060"},{"key":"ref18","first-page":"1","article-title":"Continual learning with deep generative replay","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Shin"},{"key":"ref19","first-page":"3987","article-title":"Continual learning through synaptic intelligence","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zenke"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58529-7_16"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00092"},{"key":"ref23","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref24","first-page":"1","article-title":"SCAFFOLD: Stochastic controlled averaging for on-device federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref25","first-page":"7252","article-title":"Bayesian nonparametric federated learning of neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yurochkin"},{"key":"ref26","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"Hinton","year":"2012","journal-title":"arXiv:1207.0580"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3083089"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3015157"},{"key":"ref30","first-page":"1","article-title":"Multimodal deep learning","volume-title":"Proc. ICML","author":"Ngiam"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3016820"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00817"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00810"},{"key":"ref34","first-page":"1","article-title":"Lifelong learning with dynamically expandable networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Yoon"},{"key":"ref35","first-page":"1","article-title":"Efficient lifelong learning with A-GEM","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Chaudhry"},{"key":"ref36","first-page":"5962","article-title":"Memory replay GANs: Learning to generate new categories without forgetting","volume-title":"Proc. Adv. NIPS","author":"Wu"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref38","first-page":"4652","article-title":"Overcoming catastrophic forgletting by incremental moment matching","volume-title":"Proc. Adv. NIPS","author":"Lee"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3007548"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_15"},{"issue":"7","key":"ref41","first-page":"38","article-title":"Distilling the knowledge in a neural network","volume":"14","author":"Hinton","year":"2015","journal-title":"Comput. Sci."},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150464"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3066051"},{"key":"ref44","first-page":"1607","article-title":"Born again neural networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Furlanello"},{"key":"ref45","first-page":"437","article-title":"Lifelong learning via progressive distillation and retrospection","volume-title":"Proc. Eur. Conf. Comput. Vis. (ECCV)","author":"Saihui"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00046"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01322"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107589"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3072041"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3144183"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920931"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472805"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71249-9_12"},{"key":"ref55","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. AISTATS","author":"McMahan"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2019.2944481"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2953131"},{"key":"ref58","first-page":"1","article-title":"Federated learning with matched averaging","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref59","article-title":"Federated meta-learning with fast convergence and efficient communication","author":"Chen","year":"2018","journal-title":"arXiv:1802.07876"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-67661-2_21"},{"key":"ref61","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018","journal-title":"arXiv:1806.00582"},{"key":"ref62","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume-title":"Proc. NIPS","volume":"33","author":"Lin"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00115"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2019.00049"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553517"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16213"},{"key":"ref68","first-page":"1","article-title":"Large scale distributed neural network training through online distillation","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Anil"},{"issue":"4","key":"ref69","first-page":"1","article-title":"Learning multiple layers of features from tiny images","volume":"1","author":"Krizhevsky","year":"2009","journal-title":"Handbook Systemic Autoimmune Diseases"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01226"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022859003006"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2878958"},{"key":"ref74","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019","journal-title":"arXiv:1909.06335"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10517792\/09932643.pdf?arnumber=9932643","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T18:49:21Z","timestamp":1714762161000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9932643\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":74,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3214573","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5]]}}}