{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:33:35Z","timestamp":1773246815410,"version":"3.50.1"},"reference-count":60,"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"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1109\/tmc.2023.3282941","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T18:03:47Z","timestamp":1685988227000},"page":"3923-3937","source":"Crossref","is-referenced-by-count":8,"title":["MEC-DA: Memory-Efficient Collaborative Domain Adaptation for Mobile Edge Devices"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0535-3310","authenticated-orcid":false,"given":"Xiaochen","family":"Zhou","sequence":"first","affiliation":[{"name":"UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3599-4605","authenticated-orcid":false,"given":"Yuchuan","family":"Tian","sequence":"additional","affiliation":[{"name":"UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1353-1420","authenticated-orcid":false,"given":"Xudong","family":"Wang","sequence":"additional","affiliation":[{"name":"UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, China"}]}],"member":"263","reference":[{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2976762"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref6","first-page":"11711","article-title":"MCUNet: Tiny deep learning on IoT devices","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_35"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_36"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"ref10","first-page":"106","article-title":"DARTS: Differentiable architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref11","first-page":"131","volume-title":"Covariate Shift and Local Learning by Distribution Matching","author":"Gretton","year":"2009"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00503"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6054"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6123"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00966"},{"key":"ref16","first-page":"6028","article-title":"Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liang"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00127"},{"key":"ref18","first-page":"11285","article-title":"TinyTL: Reduce memory, not parameters for efficient on-device learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cai"},{"key":"ref19","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/s0079-7421(08)60536-8"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8851810"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/291"},{"key":"ref24","article-title":"ADMP: An adversarial double masks based pruning framework for unsupervised cross-domain compression","author":"Feng","year":"2020"},{"key":"ref25","first-page":"1","article-title":"Domain adaptation regularization for spectral pruning","author":"Dillard","year":"2020","journal-title":"Proc. Brit. Mach. Vis. Conf."},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206989"},{"key":"ref27","first-page":"12991","article-title":"LST: Ladder side-tuning for parameter and memory efficient transfer learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sung"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref29","first-page":"70","article-title":"Federated adversarial domain adaptation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Peng"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00066"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00040"},{"key":"ref33","first-page":"5966","article-title":"Memory replay GANs: Learning to generate new categories without forgetting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref34","first-page":"9516","article-title":"Co2l: Contrastive continual learning","volume-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","author":"Cha"},{"key":"ref35","first-page":"934","article-title":"Representational continuity for unsupervised continual learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Madaan"},{"key":"ref36","first-page":"12073","article-title":"Federated continual learning with weighted inter-client transfer","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Yoon"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3090260"},{"key":"ref38","first-page":"775","article-title":"Discriminative clustering by regularized information maximization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Krause"},{"key":"ref39","first-page":"1558","article-title":"Learning discrete representations via information maximizing self-augmented training","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hu"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref41","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref42","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref43","first-page":"14068","article-title":"Group knowledge transfer: Federated learning of large CNNs at the edge","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"He"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1561\/9781601984616"},{"key":"ref45","first-page":"201","article-title":"Why does unsupervised pre-training help deep learning?","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Erhan"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref48","article-title":"iDLG: Improved deep leakage from gradients,","author":"Zhao","year":"2020"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref51","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref52","first-page":"1647","article-title":"Conditional adversarial domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Long"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"key":"ref55","first-page":"901","article-title":"Weight normalization: A simple reparameterization to accelerate training of deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Salimans"},{"key":"ref56","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ganin"},{"key":"ref57","article-title":"FedML: A research library and benchmark for federated machine learning","author":"He","year":"2020"},{"key":"ref58","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref59","first-page":"2595","article-title":"Parallelized stochastic gradient descent","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zinkevich"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00784"},{"key":"ref61","article-title":"ProxylessNAS: Direct neural architecture search on target task and hardware","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Cai"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7755\/10491282\/10144399.pdf?arnumber=10144399","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T20:06:58Z","timestamp":1712693218000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10144399\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":60,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2023.3282941","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5]]}}}