{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T16:01:33Z","timestamp":1784044893179,"version":"3.55.0"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"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":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9533358","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T21:27:41Z","timestamp":1632173261000},"page":"1-8","source":"Crossref","is-referenced-by-count":24,"title":["Federated Variational Autoencoder for Collaborative Filtering"],"prefix":"10.1109","author":[{"given":"Mirko","family":"Polato","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"To drop or not to drop: Robustness, consistency and differential privacy properties of dropout","volume":"abs 1503 2031","author":"jain","year":"2015","journal-title":"ArXiv"},{"key":"ref38","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref33","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"AISTATS"},{"key":"ref32","article-title":"Towards federated learning at scale: System design","author":"bonawitz","year":"2019","journal-title":"SysML 2019"},{"key":"ref31","article-title":"Auto-encoding variational bayes","volume":"abs 1312 6114","author":"kingma","year":"2014","journal-title":"CoRR"},{"key":"ref30","article-title":"beta-vae: Learning basic visual concepts with a constrained variational framework","author":"higgins","year":"2017","journal-title":"ICLRE"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref36","article-title":"Practical secure aggregation for federated learning on user-held data","author":"bonawitz","year":"0","journal-title":"NIPS Workshop on Private Multi-Party Machine Learning PMPML '16"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1900506"},{"key":"ref34","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","author":"blanchard","year":"2017","journal-title":"NIPS"},{"key":"ref10","article-title":"Federated learning: Strategies for improving communication efficiency","author":"konecny","year":"0","journal-title":"NIPS Workshop on Private Multi-Party Machine Learning PMPML '16"},{"key":"ref40","article-title":"Input perturbation: A new paradigm between central and local differential privacy","volume":"abs 2002 8570","author":"kang","year":"2020","journal-title":"ArXiv"},{"key":"ref11","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"AISTATS"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3365109.3368788"},{"key":"ref13","author":"chen","year":"2020","journal-title":"Robust federated recommendation system"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148791"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7637-6"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2012.2190726"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-016-0020-2"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2792838.2800173"},{"key":"ref28","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"3rd International Conference on Learning Representations ICLR 2015"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2897845.2897875"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487589"},{"key":"ref3","article-title":"Secure federated matrix factorization","volume":"abs 1906 5108","author":"chai","year":"2019","journal-title":"ArXiv"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3214303"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.1986.25"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/668"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3375402"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109881"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.11.018"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013193"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v38i3.2741"},{"key":"ref20","author":"ud din","year":"2019","journal-title":"Federated collaborative filtering for privacy-preserving personalized recommendation system"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"ref24","author":"ribero","year":"2020","journal-title":"Federating recommendations using differentially private prototypes"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref26","first-page":"1257","article-title":"Probabilistic matrix factorization","author":"salakhutdinov","year":"2007","journal-title":"Proceedings of the 20th International Conference on Neural Information Processing Systems NIPS'07"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.3017205"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","location":"Shenzhen, China","start":{"date-parts":[[2021,7,18]]},"end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09533358.pdf?arnumber=9533358","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:45:52Z","timestamp":1652197552000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533358\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533358","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}