{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:27:24Z","timestamp":1764937644940,"version":"build-2065373602"},"reference-count":14,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T00:00:00Z","timestamp":1698019200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T00:00:00Z","timestamp":1698019200000},"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":[[2023,10,23]]},"DOI":"10.1109\/isncc58260.2023.10323644","type":"proceedings-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T14:36:01Z","timestamp":1701095761000},"page":"1-5","source":"Crossref","is-referenced-by-count":2,"title":["Adaptive Ratio-Based-Threshold Gradient Sparsification Scheme for Federated Learning"],"prefix":"10.1109","author":[{"given":"Jeong Min","family":"Kong","sequence":"first","affiliation":[{"name":"University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elvino","family":"Sousa","sequence":"additional","affiliation":[{"name":"University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artificial Intelligence and Statistics","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.25977"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01189"},{"key":"ref4","first-page":"5973","article-title":"The convergence of sparsified gradient methods","volume-title":"Proc. Neural Inf. Process. Syst. (NeurIPS)","author":"Alistarh"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS47774.2020.00026"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.3042094"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d17-1045"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MLHPC.2016.004"},{"key":"ref9","article-title":"FedTiny: Pruned federated learning towards specialized tiny models","author":"Huang","year":"2022","journal-title":"ar Xiv preprint"},{"key":"ref10","article-title":"Lot-teryFL: Personalized and communication-efficient federated learning with Lottery Ticket Hypothesis on non-IID datasets","author":"Li","year":"2020","journal-title":"arXiv preprint"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3063291"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.21437\/interspeech.2015-354"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"journal-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref14"}],"event":{"name":"2023 International Symposium on Networks, Computers and Communications (ISNCC)","start":{"date-parts":[[2023,10,23]]},"location":"Doha, Qatar","end":{"date-parts":[[2023,10,26]]}},"container-title":["2023 International Symposium on Networks, Computers and Communications (ISNCC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10323591\/10323546\/10323644.pdf?arnumber=10323644","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T17:14:21Z","timestamp":1761326061000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10323644\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,23]]},"references-count":14,"URL":"https:\/\/doi.org\/10.1109\/isncc58260.2023.10323644","relation":{},"subject":[],"published":{"date-parts":[[2023,10,23]]}}}