{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T08:05:47Z","timestamp":1767773147484,"version":"3.37.3"},"reference-count":17,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001807","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002170,62071222"],"award-info":[{"award-number":["62002170,62071222"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,28]]},"DOI":"10.1109\/icc45041.2023.10278660","type":"proceedings-article","created":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T17:54:10Z","timestamp":1698083650000},"page":"1768-1773","source":"Crossref","is-referenced-by-count":3,"title":["Sparse Federated Training of Object Detection in the Internet of Vehicles"],"prefix":"10.1109","author":[{"given":"Luping","family":"Rao","sequence":"first","affiliation":[{"name":"Nanjing University of Science and Technology,Nanjing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan","family":"Ma","sequence":"additional","affiliation":[{"name":"Zhejiang Lab,Hang Zhou,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Ding","sequence":"additional","affiliation":[{"name":"Data61, CSIRO,Sydney,Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuwen","family":"Qian","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology,Nanjing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics,Nanjing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang Lab,Hang Zhou,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref12","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref15","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"International Conference on Machine Learning"},{"key":"ref14","article-title":"Yolov3: An incremental improvement","author":"redmon","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref11","article-title":"Dispfl: Towards communication-efficient personalized federated learning via decentral-ized sparse training","author":"dai","year":"2022","journal-title":"ArXiv Preprint"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485929"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2970553"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.1109\/TITS.2020.3025687","article-title":"An edge traffic flow detection scheme based on deep learning in an intelligent transportation system","volume":"22","author":"chen","year":"2021","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.5244\/C.25.75"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.282"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref9","article-title":"Fedkd: Communication efficient federated learning via knowledge distillation","volume":"abs 2108 13323","author":"wu","year":"2021","journal-title":"CoRR"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref3","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"konecny","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref6","article-title":"Distilling the knowledge in a neural network","volume":"2","author":"hinton","year":"2015","journal-title":"ArXiv Preprint"},{"key":"ref5","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"han","year":"2015","journal-title":"ArXiv Preprint"}],"event":{"name":"ICC 2023 - IEEE International Conference on Communications","start":{"date-parts":[[2023,5,28]]},"location":"Rome, Italy","end":{"date-parts":[[2023,6,1]]}},"container-title":["ICC 2023 - IEEE International Conference on Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10278505\/10278554\/10278660.pdf?arnumber=10278660","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T19:00:03Z","timestamp":1699902003000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10278660\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,28]]},"references-count":17,"URL":"https:\/\/doi.org\/10.1109\/icc45041.2023.10278660","relation":{},"subject":[],"published":{"date-parts":[[2023,5,28]]}}}