{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:46:03Z","timestamp":1774421163105,"version":"3.50.1"},"reference-count":17,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"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":[[2021,9,27]]},"DOI":"10.1109\/spawc51858.2021.9593194","type":"proceedings-article","created":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T01:23:47Z","timestamp":1636766627000},"page":"281-285","source":"Crossref","is-referenced-by-count":23,"title":["Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning"],"prefix":"10.1109","author":[{"given":"Chung-Hsuan","family":"Hu","sequence":"first","affiliation":[]},{"given":"Zheng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Erik G.","family":"Larsson","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3052681"},{"key":"ref11","first-page":"1","article-title":"Data-importance aware user scheduling for communication-efficient edge machine learning","author":"liu","year":"2020","journal-title":"IEEE Trans on Cognitive Communications and Networking"},{"key":"ref12","article-title":"Federated learning in unreliable and resource-constrained cellular wireless networks","author":"salehi","year":"2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053740"},{"key":"ref14","article-title":"Adaptive transmission scheduling in wireless networks for asynchronous federated learning","year":"2021"},{"key":"ref15","article-title":"MNIST handwritten digit database","author":"lecun","year":"2010"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1561\/1300000060"},{"key":"ref17","article-title":"On the convergence of fedavg on non-iid data","author":"li","year":"2020","journal-title":"International Conference on Learning Representations"},{"key":"ref4","article-title":"Staleness-aware asyncsgd for distributed deep learning","author":"zhang","year":"2016","journal-title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"},{"key":"ref3","article-title":"Revisiting distributed synchronous SGD","author":"chen","year":"2016","journal-title":"International Conference on Learning Representations Workshops Track"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378161"},{"key":"ref5","article-title":"Asynchronous federated optimization","author":"xie","year":"2020","journal-title":"NeurIPS workshop on Optimization for Machine Learning"},{"key":"ref8","article-title":"Federated learning: A signal processing perspective","author":"gafni","year":"2021"},{"key":"ref7","article-title":"FedAT: A communication-efficient federated learning method with asynchronous tiers under non-iid data","author":"chai","year":"2020"},{"key":"ref2","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":"ref1","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"kone cny","year":"2016"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2944169"}],"event":{"name":"2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","location":"Lucca, Italy","start":{"date-parts":[[2021,9,27]]},"end":{"date-parts":[[2021,9,30]]}},"container-title":["2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9593094\/9593095\/09593194.pdf?arnumber=9593194","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:50:10Z","timestamp":1652201410000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9593194\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,27]]},"references-count":17,"URL":"https:\/\/doi.org\/10.1109\/spawc51858.2021.9593194","relation":{},"subject":[],"published":{"date-parts":[[2021,9,27]]}}}