{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:52:33Z","timestamp":1757314353531,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":15,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation","award":["CNS-2007995","CNS-2008145","ECCS-2139508","CNS-2236449"],"award-info":[{"award-number":["CNS-2007995","CNS-2008145","ECCS-2139508","CNS-2236449"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1145\/3586209.3591399","type":"proceedings-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T16:11:55Z","timestamp":1687968715000},"page":"21-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Approximate Wireless Communication for Federated Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0401-7101","authenticated-orcid":false,"given":"Xiang","family":"Ma","sequence":"first","affiliation":[{"name":"Utah State University, Logan, UT, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0680-147X","authenticated-orcid":false,"given":"Haijian","family":"Sun","sequence":"additional","affiliation":[{"name":"University of Georgia, Athens, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1571-3631","authenticated-orcid":false,"given":"Rose Qingyang","family":"Hu","sequence":"additional","affiliation":[{"name":"Utah State University, Logan, UT, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5671-916X","authenticated-orcid":false,"given":"Yi","family":"Qian","sequence":"additional","affiliation":[{"name":"University of Nebraska-Lincoln, Lincoln, NE, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Sparse communication for distributed gradient descent. arXiv preprint arXiv:1704.05021","author":"Aji Alham Fikri","year":"2017","unstructured":"Alham Fikri Aji and Kenneth Heafield. 2017. Sparse communication for distributed gradient descent. arXiv preprint arXiv:1704.05021 (2017)."},{"key":"e_1_3_2_1_2_1","volume-title":"Minimum distances of the QC-LDPC Codes in IEEE 802 Communication Standards. arXiv preprint arXiv:1602.02831","author":"Butler Brian K","year":"2016","unstructured":"Brian K Butler. 2016. Minimum distances of the QC-LDPC Codes in IEEE 802 Communication Standards. arXiv preprint arXiv:1602.02831 (2016)."},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLRWorkshop and Conference Proceedings, 249--256","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLRWorkshop and Conference Proceedings, 249--256."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_3_2_1_5_1","unstructured":"Jakub Konen"},{"key":"e_1_3_2_1_6_1","volume-title":"Ananda Theertha Suresh, and Dave Bacon","author":"McMahan H Brendan","year":"2016","unstructured":"y, H Brendan McMahan, Felix X Yu, Peter Richt\u00e1rik, Ananda Theertha Suresh, and Dave Bacon. 2016. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)."},{"key":"e_1_3_2_1_7_1","first-page":"297","article-title":"An efficient statistical-based gradient compression technique for distributed training systems","volume":"3","author":"Abdelmoniem Ahmed M","year":"2021","unstructured":"Ahmed M Abdelmoniem, Ahmed Elzanaty, Mohamed-Slim Alouini, and Marco Canini. 2021. An efficient statistical-based gradient compression technique for distributed training systems. Proceedings of Machine Learning and Systems 3 (2021), 297--322.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_8_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR 1273--1282."},{"key":"e_1_3_2_1_9_1","volume-title":"Activation functions: Comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378","author":"Nwankpa Chigozie","year":"2018","unstructured":"Chigozie Nwankpa, Winifred Ijomah, Anthony Gachagan, and Stephen Marshall. 2018. Activation functions: Comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378 (2018)."},{"key":"e_1_3_2_1_10_1","volume-title":"SAP: an architecture for selectively approximate wireless communication. arXiv preprint arXiv:1510.03955","author":"Ransford Benjamin","year":"2015","unstructured":"Benjamin Ransford and Luis Ceze. 2015. SAP: an architecture for selectively approximate wireless communication. arXiv preprint arXiv:1510.03955 (2015)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Frank Seide Hao Fu Jasha Droppo Gang Li and Dong Yu. 2014. 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns. In Fifteenth annual conference of the international speech communication association.","DOI":"10.21437\/Interspeech.2014-274"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1851182.1851187"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2022.3167094"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3027306"},{"key":"e_1_3_2_1_15_1","volume-title":"Terngrad: Ternary gradients to reduce communication in distributed deep learning. Advances in neural information processing systems 30","author":"Xu Cong","year":"2017","unstructured":"WeiWen, Cong Xu, Feng Yan, ChunpengWu, YandanWang, Yiran Chen, and Hai Li. 2017. Terngrad: Ternary gradients to reduce communication in distributed deep learning. Advances in neural information processing systems 30 (2017)."}],"event":{"name":"WiSec '23: 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control","SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing"],"location":"Guildford United Kingdom","acronym":"WiSec '23"},"container-title":["Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3586209.3591399","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3586209.3591399","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:32Z","timestamp":1750178252000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3586209.3591399"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6]]},"references-count":15,"alternative-id":["10.1145\/3586209.3591399","10.1145\/3586209"],"URL":"https:\/\/doi.org\/10.1145\/3586209.3591399","relation":{},"subject":[],"published":{"date-parts":[[2023,6]]},"assertion":[{"value":"2023-06-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}