{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T15:43:34Z","timestamp":1745509414513,"version":"3.37.3"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002766","name":"Beijing University of Posts and Telecommunications","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002766","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,14]]},"DOI":"10.1109\/bmsb58369.2023.10211485","type":"proceedings-article","created":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T17:21:10Z","timestamp":1692206470000},"page":"1-5","source":"Crossref","is-referenced-by-count":1,"title":["Federated Learning Technology in Serial Topology for IoT Networks"],"prefix":"10.1109","author":[{"given":"Jianhang","family":"Sheng","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University,Department of Electronic Engineering,Shanghai,China"}]},{"given":"Jian","family":"Xiong","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University,Department of Electronic Engineering,Shanghai,China"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney,School of Computer Science,Ultimo,NSW,Australia"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/BMSB49480.2020.9379878"},{"journal-title":"Expanding the reach of federated learning by reducing client resource requirements","year":"2018","author":"caldas","key":"ref12"},{"journal-title":"LEAF A benchmark for federated settings","year":"2018","author":"caldas","key":"ref23"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/e22030314"},{"journal-title":"Edge-assisted hierarchical federated learning with non-iid data","year":"2019","author":"liu","key":"ref14"},{"key":"ref20","article-title":"Cola: Decentralized linear learning","volume":"31","author":"he","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2978082"},{"journal-title":"Adaptive Federated Optimization","year":"2020","author":"reddi","key":"ref22"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"journal-title":"Decentralized bayesian learning over graphs","year":"2019","author":"lalitha","key":"ref21"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref1","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":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.2975189"},{"key":"ref16","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"li","year":"2020","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2978082"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9030440"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0230706"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/BMSB49480.2020.9379862"},{"key":"ref9","first-page":"2021","article-title":"Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization","author":"reisizadeh","year":"2020","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/BMSB53066.2021.9547143"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3035431"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/BMSB55706.2022.9828687"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/BMSB55706.2022.9828732"}],"event":{"name":"2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","start":{"date-parts":[[2023,6,14]]},"location":"Beijing, China","end":{"date-parts":[[2023,6,16]]}},"container-title":["2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10210852\/10211094\/10211485.pdf?arnumber=10211485","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T17:44:50Z","timestamp":1693849490000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10211485\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,14]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/bmsb58369.2023.10211485","relation":{},"subject":[],"published":{"date-parts":[[2023,6,14]]}}}