{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T22:19:42Z","timestamp":1768688382126,"version":"3.49.0"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"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":[[2020,8]]},"DOI":"10.1109\/icccn49398.2020.9209670","type":"proceedings-article","created":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T20:59:41Z","timestamp":1601499581000},"page":"1-9","source":"Crossref","is-referenced-by-count":32,"title":["MLGuard: Mitigating Poisoning Attacks in Privacy Preserving Distributed Collaborative Learning"],"prefix":"10.1109","author":[{"given":"Youssef","family":"Khazbak","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianxiang","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guohong","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP.2012.6459985"},{"key":"ref11","article-title":"Man vs. machine: Practical adversarial detection of malicious crowdsourcing workers","author":"wang","year":"2014","journal-title":"Usenix Security"},{"key":"ref12","article-title":"Poisoning attacks against support vector machines","author":"biggio","year":"2012","journal-title":"International Conference on Machine Learning"},{"key":"ref13","article-title":"Mitigating sybils in federated learning poisoning","author":"fung","year":"2018"},{"key":"ref14","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","author":"blanchard","year":"2017","journal-title":"NIPS"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32009-5_38"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40203-6_1"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2991079.2991125"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48910-X_16"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref4","article-title":"Federated learning for mobile keyboard prediction","author":"hard","year":"2018"},{"key":"ref3","article-title":"Towards end-to-end speech recognition with recurrent neural networks","author":"graves","year":"2014","journal-title":"International Conference on Machine Learning"},{"key":"ref6","article-title":"Privacy-preserving deep learning","author":"shokri","year":"2015","journal-title":"ACM CCS"},{"key":"ref5","article-title":"Deanonymizing mobility traces with colocation information","author":"khazbak","year":"2017","journal-title":"IEEE CNS"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref7","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics"},{"key":"ref2","article-title":"Deep speech: Scaling up end-to-end speech recognition","author":"hannun","year":"2014"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref20","article-title":"Learning multiple layers of features from tiny images. technical report","author":"krizhevsky","year":"2009","journal-title":"Tech Rep"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3302505.3310070"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/DSN.2018.00064"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/2426656.2426663"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3302505.3310083"},{"key":"ref25","article-title":"How to backdoor federated learning","author":"bagdasaryan","year":"2018"}],"event":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","location":"Honolulu, HI, USA","start":{"date-parts":[[2020,8,3]]},"end":{"date-parts":[[2020,8,6]]}},"container-title":["2020 29th International Conference on Computer Communications and Networks (ICCCN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9205796\/9209588\/09209670.pdf?arnumber=9209670","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:16:10Z","timestamp":1656375370000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9209670\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/icccn49398.2020.9209670","relation":{},"subject":[],"published":{"date-parts":[[2020,8]]}}}