{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T01:27:27Z","timestamp":1752283647723},"reference-count":56,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"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":[[2022,11,1]]},"DOI":"10.1109\/itw54588.2022.9965815","type":"proceedings-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T20:47:00Z","timestamp":1670446020000},"page":"410-415","source":"Crossref","is-referenced-by-count":10,"title":["Private Federated Submodel Learning with Sparsification"],"prefix":"10.1109","author":[{"given":"Sajani","family":"Vithana","sequence":"first","affiliation":[{"name":"University of Maryland,Department of Electrical and Computer Engineering,College Park,MD,20742"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sennur","family":"Ulukus","sequence":"additional","affiliation":[{"name":"University of Maryland,Department of Electrical and Computer Engineering,College Park,MD,20742"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2019.2900313"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2018.2869154"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2018.2828310"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2017.2777490"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2021.3125006"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2019.2948845"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2020.3013152"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT44484.2020.9174126"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2021.3136583"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2021.3085363"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2019.2918207"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2018.2848977"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref1","article-title":"Communication efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"AISTATS"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT50566.2022.9834439"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419188"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.icte.2022.02.008"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/293347.293350"},{"article-title":"Secure federated submodel learning","year":"0","author":"niu","key":"ref23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2018.2791994"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2017.2689028"},{"key":"ref50","article-title":"cpSGD: Communication-efficient and differentially-private distributed SGD","author":"agarwal","year":"2018","journal-title":"NeurIPS"},{"key":"ref51","article-title":"Privacy amplification by subsampling: Tight analyses via couplings and divergences","author":"balle","year":"2018","journal-title":"NeurIPS"},{"key":"ref56","article-title":"Differentially private federated learning: A client level perspective","author":"geyer","year":"2017","journal-title":"NeurIPS"},{"key":"ref55","article-title":"Shuffled model of differential privacy in federated learning","author":"girgis","year":"2021","journal-title":"AISTAT"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-26951-7_22"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975482.151"},{"key":"ref52","article-title":"Differentially private federated learning: An information-theoretic perspective","author":"asoodeh","year":"2020","journal-title":"ICML-FL"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45855.2022.9839279"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT45174.2021.9518221"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2985917"},{"key":"ref13","article-title":"The convergence of sparsified gradient methods","author":"alistarh","year":"2018","journal-title":"NeurIPS"},{"key":"ref14","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","author":"alistarh","year":"2017","journal-title":"NeurIPS"},{"key":"ref15","article-title":"Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization","author":"reisizadeh","year":"2020","journal-title":"AISTATS"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054168"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICC42927.2021.9500855"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45855.2022.9839200"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2022.3165400"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148987"},{"key":"ref5","article-title":"Gradient sparsification for communication-efficient distributed optimization","author":"wangni","year":"2018","journal-title":"NeurIPS"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS47774.2020.00026"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.3042094"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP49062.2020.9231531"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/473"},{"key":"ref46","article-title":"The secret sharer: Evaluating and testing unintended memorization in neural networks","author":"carlini","year":"2019","journal-title":"USENIX"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"article-title":"Locally private distributed reinforcement learning","year":"0","author":"ono","key":"ref48"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref42","article-title":"Deep leakage from gradients","author":"zhu","year":"2019","journal-title":"NeurIPS"},{"key":"ref41","article-title":"Inverting gradients&#x2013;how easy is it to break privacy in federated learning?","author":"geiping","year":"2020","journal-title":"NeurIPS"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3142358"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737416"}],"event":{"name":"2022 IEEE Information Theory Workshop (ITW)","start":{"date-parts":[[2022,11,1]]},"location":"Mumbai, India","end":{"date-parts":[[2022,11,9]]}},"container-title":["2022 IEEE Information Theory Workshop (ITW)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9965754\/9965755\/09965815.pdf?arnumber=9965815","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T19:42:29Z","timestamp":1672083749000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9965815\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,1]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/itw54588.2022.9965815","relation":{},"subject":[],"published":{"date-parts":[[2022,11,1]]}}}