{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T05:30:18Z","timestamp":1745559018035,"version":"3.28.0"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"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":[],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1109\/icassp40776.2020.9054634","type":"proceedings-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T20:21:13Z","timestamp":1586463673000},"page":"8866-8870","source":"Crossref","is-referenced-by-count":240,"title":["Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks"],"prefix":"10.1109","author":[{"given":"M. S. H.","family":"Abad","sequence":"first","affiliation":[]},{"given":"E.","family":"Ozfatura","sequence":"additional","affiliation":[]},{"given":"D.","family":"GUndUz","sequence":"additional","affiliation":[]},{"given":"O.","family":"Ercetin","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP45357.2019.8969185"},{"key":"ref11","article-title":"Accelerating DNN training in wireless federated edge learning system","volume":"abs 1905 9712","author":"ren","year":"2019","journal-title":"CoRR"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737464"},{"article-title":"Don’t use large mini-batches, use local SGD","year":"2018","author":"lin","key":"ref13"},{"article-title":"A distributed hierarchical SGD algorithm with sparse global reduction","year":"2019","author":"zhou","key":"ref14"},{"key":"ref15","article-title":"Edge-assisted hierarchical federated learning with non-iid data","volume":"abs 1905 6641","author":"liu","year":"2019","journal-title":"CoRR"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/26.634685"},{"key":"ref17","article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","author":"lin","year":"2018","journal-title":"International Conference on Learning Representations"},{"article-title":"Infso-ict-247733 earth deliverable d 2. 3 energy efficiency analysis of the reference systems, areas of improvements and target breakdown","year":"2012","author":"wajda","key":"ref18"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref19"},{"key":"ref4","article-title":"Wireless network intelligence at the edge","volume":"abs 1812 2858","author":"park","year":"2018","journal-title":"CoRR"},{"article-title":"Lowlatency broadband analog aggregation for federated edge learning","year":"2018","author":"zhu","key":"ref3"},{"article-title":"Towards federated learning at scale: System design","year":"2019","author":"bonawitz","key":"ref6"},{"key":"ref5","first-page":"1273","article-title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","volume":"54","author":"mcmahan","year":"2017","journal-title":"Proc of the International Conference on Artificial Intelligence and Statistics"},{"article-title":"Machine learning in the air","year":"2019","author":"g\u00fcnd\u00fcz","key":"ref8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2919837"},{"article-title":"Federated learning via over-the-air computation","year":"2018","author":"yang","key":"ref2"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC.2019.8904164"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2019.8849334"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref22","article-title":"Federated learning with non-iid data","volume":"abs 1806 582","author":"zhao","year":"2018","journal-title":"CoRR"},{"article-title":"Accurate, large minibatch SGD: training imagenet in 1 hour","year":"2017","author":"goyal","key":"ref21"},{"key":"ref23","article-title":"Robust and communication- efficient federated learning from non-iid data","volume":"abs 1903 2891","author":"sattler","year":"2019","journal-title":"CoRR"}],"event":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","start":{"date-parts":[[2020,5,4]]},"location":"Barcelona, Spain","end":{"date-parts":[[2020,5,8]]}},"container-title":["ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9040208\/9052899\/09054634.pdf?arnumber=9054634","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:24:35Z","timestamp":1656375875000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9054634\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/icassp40776.2020.9054634","relation":{},"subject":[],"published":{"date-parts":[[2020,5]]}}}