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pro vyhledávání: '"Zhao, Joshua C."'
Autor:
Zhao, Joshua C., Bagchi, Saurabh, Avestimehr, Salman, Chan, Kevin S., Chaterji, Somali, Dimitriadis, Dimitris, Li, Jiacheng, Li, Ninghui, Nourian, Arash, Roth, Holger R.
Deep learning has shown incredible potential across a vast array of tasks and accompanying this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices and recen
Externí odkaz:
http://arxiv.org/abs/2405.03636
Federated learning is a decentralized learning paradigm introduced to preserve privacy of client data. Despite this, prior work has shown that an attacker at the server can still reconstruct the private training data using only the client updates. Th
Externí odkaz:
http://arxiv.org/abs/2403.18144
Autor:
Zhao, Joshua C., Elkordy, Ahmed Roushdy, Sharma, Atul, Ezzeldin, Yahya H., Avestimehr, Salman, Bagchi, Saurabh
Secure aggregation promises a heightened level of privacy in federated learning, maintaining that a server only has access to a decrypted aggregate update. Within this setting, linear layer leakage methods are the only data reconstruction attacks abl
Externí odkaz:
http://arxiv.org/abs/2303.14868
Autor:
Zhao, Joshua C., Sharma, Atul, Elkordy, Ahmed Roushdy, Ezzeldin, Yahya H., Avestimehr, Salman, Bagchi, Saurabh
Federated learning was introduced to enable machine learning over large decentralized datasets while promising privacy by eliminating the need for data sharing. Despite this, prior work has shown that shared gradients often contain private informatio
Externí odkaz:
http://arxiv.org/abs/2303.12233
Autor:
Hinojosa, Cecilia A., Sitar, Siara I., Zhao, Joshua C., Barbosa, Joshua D., Hien, Denise A., Welsh, Justine W., Fani, Negar, van Rooij, Sanne J.H.
Publikováno v:
Chronic Stress; 6/4/2024, p1-16, 16p
Autor:
Zhao, Joshua C., Sharma, Atul, Elkordy, Ahmed Roushdy, Ezzeldin, Yahya H., Avestimehr, Salman, Bagchi, Saurabh
Security and privacy are important concerns in machine learning. End user devices often contain a wealth of data and this information is sensitive and should not be shared with servers or enterprises. As a result, federated learning was introduced to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0570c71fd18ff7f749150d0781ec0c71
Akademický článek
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