{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T17:30:00Z","timestamp":1783791000041,"version":"3.55.0"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1900654"],"award-info":[{"award-number":["1900654"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1649372"],"award-info":[{"award-number":["1649372"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1526736"],"award-info":[{"award-number":["1526736"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2022,10,1]]},"DOI":"10.1109\/tmc.2021.3058627","type":"journal-article","created":{"date-parts":[[2021,2,11]],"date-time":"2021-02-11T21:52:07Z","timestamp":1613080327000},"page":"3609-3628","source":"Crossref","is-referenced-by-count":47,"title":["FedPacket: A Federated Learning Approach to Mobile Packet Classification"],"prefix":"10.1109","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7749-2793","authenticated-orcid":false,"given":"Evita","family":"Bakopoulou","sequence":"first","affiliation":[{"name":"University of California, Irvine, Irvine, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6492-7145","authenticated-orcid":false,"given":"Balint","family":"Tillman","sequence":"additional","affiliation":[{"name":"Google, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Athina","family":"Markopoulou","sequence":"additional","affiliation":[{"name":"University of California, Irvine, Irvine, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2018-0030"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2742647.2742668"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2619091"},{"key":"ref6","article-title":"Haystack: A multi-purpose mobile vantage point in user space","author":"Razaghpanah","year":"2015"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906392"},{"key":"ref8","article-title":"AntMonitor: System and applications","author":"Shuba","year":"2016"},{"key":"ref9","article-title":"AntShield: On-device detection of personal information exposure","author":"Shuba","year":"2018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/SPAWC.2018.8445948"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2018-0035"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2020-0017"},{"key":"ref15","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref19","article-title":"Comprehensive privacy analysis of deep learning: Stand-alone and federated learning under passive and active white-box inference attacks","author":"Nasr","year":"2018"},{"key":"ref20","first-page":"14 747","article-title":"Deep leakage from gradients","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhu"},{"key":"ref21","article-title":"Inverting gradients\u2013how easy is it to break privacy in federated learning?","author":"Geiping","year":"2020"},{"key":"ref22","article-title":"A framework for evaluating gradient leakage attacks in federated learning","author":"Wei","year":"2020"},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.1145\/3133956.3133982","article-title":"Practical secure aggregation for privacy preserving machine learning","volume-title":"Eprint.Iacr.Org","author":"Bonawitz"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23353"},{"key":"ref25","article-title":"Bug fixes, improvements,... and privacy leaks","author":"Ren","year":"2018"},{"key":"ref26","article-title":"Tracking the trackers: Towards understanding the mobile advertising and tracking ecosystem","author":"Vallina-Rodriguez","year":"2016"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3091478.3091514"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2015-0018"},{"key":"ref31","article-title":"PrivacyProxy: Leveraging crowdsourcing and in situ traffic analysis to detect and mitigate information leakage","volume-title":"CoRR","author":"Srivastava","year":"2017"},{"key":"ref32","first-page":"1391","article-title":"A privacy analysis of cross-device tracking","volume-title":"Proc. 26th USENIX Security Symp.","author":"Zimmeck"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"ref34","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Kone\u010dn\u1ef3","year":"2016"},{"key":"ref35","article-title":"Expanding the reach of federated learning by reducing client resource requirements","volume-title":"CoRR","author":"Caldas","year":"2018"},{"key":"ref36","article-title":"One-shot federated learning","author":"Guha","year":"2019"},{"key":"ref37","article-title":"Towards federated learning at scale: System design","author":"Bonawitz","year":"2019"},{"key":"ref38","article-title":"Federated learning for mobile keyboard prediction","author":"Hard","year":"2018"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3058627"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref42","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-43883-8_8"},{"key":"ref44","first-page":"11 849","article-title":"Online continual learning with maximal interfered retrieval","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Aljundi"},{"key":"ref45","first-page":"11 816","article-title":"Gradient based sample selection for online continual learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Aljundi"},{"key":"ref46","article-title":"Overcoming forgetting in federated learning on non-IID data","author":"Shoham","year":"2019"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP40778.2020.9190968"},{"key":"ref48","article-title":"Federated continual learning with adaptive parameter communication","author":"Yoon","year":"2020"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2017.10"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/MSEC.2019.2935666"},{"key":"ref53","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","author":"Bhagoji"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"ref55","article-title":"iDLG: Improved deep leakage from gradients","author":"Zhao","year":"2020"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-5906-5_752"},{"key":"ref57","article-title":"Learning differentially private language models without losing accuracy","author":"McMahan","year":"2017"},{"key":"ref58","article-title":"Differentially private federated learning: A client level perspective","volume-title":"CoRR","author":"Geyer","year":"2017"},{"key":"ref59","article-title":"Protection against reconstruction and its applications in private federated learning","author":"Bhowmick","year":"2018"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3338501.3357370"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-010-0420-4"},{"key":"ref63","article-title":"On the convergence of FedAvg on non-IID data","author":"Li","year":"2019"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref66","first-page":"161","article-title":"The tradeoffs of large scale learning","volume-title":"Proc. 20th Int. Conf. Neural Inf. Process. Syst.","author":"Bottou"},{"key":"ref67","article-title":"Stochastic gradient descent (v.2)","author":"Bottou"},{"key":"ref68","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3023905"},{"key":"ref71","article-title":"Generative models for effective ML on private, decentralized datasets","author":"Augenstein","year":"2019"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/7755\/9872067\/9352526-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7755\/9872067\/09352526.pdf?arnumber=9352526","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T23:17:58Z","timestamp":1704842278000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9352526\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,1]]},"references-count":59,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2021.3058627","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,1]]}}}