{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T19:31:37Z","timestamp":1787254297364,"version":"build-2736575974"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Ministry of Education, Singapore, under its Tier 2 Research Fund","award":["MOE-T2EP20125-0011"],"award-info":[{"award-number":["MOE-T2EP20125-0011"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62201504"],"award-info":[{"award-number":["62201504"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Yunnan Science and Technology Program","award":["202602AD080017"],"award-info":[{"award-number":["202602AD080017"]}]},{"name":"National Research Foundation, Singapore and Infocomm Media Development Authority under its Communications and Connectivity Bridging Funding Initiative"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cogn. Commun. Netw."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tccn.2026.3722832","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T19:12:21Z","timestamp":1786561941000},"page":"11403-11416","source":"Crossref","is-referenced-by-count":0,"title":["Asymmetric Partial Model Transmission for Federated Edge Learning"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0814-3391","authenticated-orcid":false,"given":"Zihan","family":"Chen","sequence":"first","affiliation":[{"name":"Singapore University of Technology and Design","place":["Changi, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0256-2416","authenticated-orcid":false,"given":"Howard H.","family":"Yang","sequence":"additional","affiliation":[{"name":"Zhejiang University\/University of Illinois Urbana\u2013Champaign Institute, Zhejiang University","place":["Haining, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4037-3149","authenticated-orcid":false,"given":"Tony Q. S.","family":"Quek","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design","place":["Changi, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5568-7610","authenticated-orcid":false,"given":"Kai Fong Ernest","family":"Chong","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design","place":["Changi, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref2","first-page":"1","article-title":"Federated learning: Strategies for improving communication efficiency","volume-title":"Proc. NIPS Workshop Private Multi-Party Mach. Learn.","author":"Kone\u010dn\u00fd"},{"key":"ref3","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.001.1900370"},{"key":"ref5","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019","journal-title":"arXiv:1909.06335"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2021.3126174"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2023.3310529"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108661"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM59602.2025.11432417"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref12","first-page":"2021","article-title":"FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization","volume-title":"Proc. Int. Conf. Artif. Intell. Statist. (AISTATS)","author":"Reisizadeh"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20555"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3166101"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2953131"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3453142.3492909"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485929"},{"key":"ref19","first-page":"1","article-title":"PerFedMask: Personalized federated learning with optimized masking vectors","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Setayesh"},{"key":"ref20","first-page":"23034","article-title":"ProgFed: Effective, communication, and computation efficient federated learning by progressive training","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Wang"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26023"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3138394"},{"key":"ref23","volume-title":"Internet Speed Around World-Speedtest Global Index Speeds","year":"2023"},{"key":"ref24","article-title":"Learning multiple layers of features from tiny images","volume-title":"Univ. Toronto, Toronto, ON, Canada, Tech. Rep.","author":"Krizhevsky","year":"2009"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298188"},{"key":"ref26","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst. (MLSys)","author":"Tian"},{"key":"ref27","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Karimireddy"},{"key":"ref28","first-page":"1","article-title":"Fair resource allocation in federated learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Tian"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00994"},{"key":"ref30","first-page":"1","article-title":"FedMix: Approximation of mixup under mean augmented federated learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Yoon"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3108197"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3309376"},{"key":"ref33","first-page":"1","article-title":"Think locally, act globally: Federated learning with local and global representations","volume-title":"Proc. NeurIPS Workshop Federated Learn.","author":"Liang"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00126"},{"key":"ref35","first-page":"1","article-title":"Dynamic attention-based communication-efficient federated learning","volume-title":"Proc. Int. Workshop Federated Transf. Learn. Data Sparsity Confidentiality (FTL-IJCAI)","author":"Chen"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2021.3114610"},{"key":"ref37","first-page":"1","article-title":"FedCV: A federated learning framework for diverse computer vision tasks","volume-title":"Proc. Int. Workshop Trustable","author":"He"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00982"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60548-3_13"},{"key":"ref40","first-page":"1","article-title":"FedBN: Federated learning on non-IID features via local batch normalization","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Li"},{"key":"ref41","first-page":"1","article-title":"Federated adversarial domain adaptation","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Peng"},{"key":"ref42","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"33","author":"Fallah"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref44","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Paszke"},{"key":"ref45","first-page":"1","article-title":"On the convergence of FedAvg on non-IID data","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Li"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2152"},{"key":"ref47","article-title":"HeteroFL: Computation and communication efficient federated learning for heterogeneous clients","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Diao"},{"key":"ref48","first-page":"29898","article-title":"Gradient inversion with generative image prior","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jeon"},{"key":"ref49","volume-title":"The Pascal Visual Object Classes Challenge 2012 (VOC2012) Results","author":"Everingham","year":"2012"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.544"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref54","first-page":"1707","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alistarh"},{"key":"ref55","first-page":"1","article-title":"FedExP: Speeding up federated averaging via extrapolation","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Jhunjhunwala"}],"container-title":["IEEE Transactions on Cognitive Communications and Networking"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6687307\/11304002\/11653277.pdf?arnumber=11653277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T19:07:38Z","timestamp":1787252858000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11653277\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/tccn.2026.3722832","relation":{},"ISSN":["2332-7731","2372-2045"],"issn-type":[{"value":"2332-7731","type":"electronic"},{"value":"2372-2045","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}