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Sen. Netw."],"published-print":{"date-parts":[[2022,11,30]]},"abstract":"<jats:p>Deep Learning (DL) is an essential technology for modern intelligent sensor network and interactive multimedia applications, having problems with user data privacy when training on a central cloud. While Federated Learning (FL) motivates to preserve user privacy, it also causes new problems of lower user terminal usability and training efficiency, which caused substantial energy consumption. This article proposes a novel energy-efficient and privacy-aware decomposition framework to improve user-side FL efficiency under pre-defined privacy requirements with the assistance of Mobile Edge Computing (MEC) and Software Decomposition. It takes the propagation of each neural layer as the migrating unit and considers the tradeoff relationship between privacy and efficiency. We also propose an online scheduling algorithm to optimize the framework\u2019s training performance. Furthermore, we summarize eight privacy-sensitive information classes on which existing privacy attacks base and design configurable privacy preservation mechanisms for each class. Simulations and experiments prove the effectiveness of our framework and algorithm in FL efficiency improvement and the effects of different privacy constraints on the overall training efficiency.<\/jats:p>","DOI":"10.1145\/3522741","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T06:22:27Z","timestamp":1648189347000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["An Energy-efficient and Privacy-aware Decomposition Framework for Edge-assisted Federated Learning"],"prefix":"10.1145","volume":"18","author":[{"given":"Yimin","family":"Shi","sequence":"first","affiliation":[{"name":"School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen and Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haihan","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen and Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, South China University of Technology, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4658-0034","authenticated-orcid":false,"given":"Wei","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen and Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437801.3441593"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2019.2958281"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"e_1_3_1_5_2","first-page":"17","volume-title":"Proceedings of the 23rd USENIX Security Symposium (USENIX Security\u201914)","author":"Fredrikson Matthew","year":"2014","unstructured":"Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart. 2014. 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