Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Basak Guler"'
Autor:
Volkan Atmis, Basak Guler
Publikováno v:
Gynecology Obstetrics & Reproductive Medicine, Vol 26, Iss 1, Pp 44-50 (2020)
OBJECTIVE: Primary end-point of this study was to detect if there is an association between walking speed and urinary incontinence in older women and secondarily to detect an association between urinary incontinence with other geriatric syndromes. S
Externí odkaz:
https://doaj.org/article/c7b4b7dd9c6f4f3fa48b7c3dda31e05b
Publikováno v:
Entropy, Vol 19, Iss 12, p 635 (2017)
We consider a two party network where each party wishes to compute a function of two correlated sources. Each source is observed by one of the parties. The true joint distribution of the sources is known to one party. The other party, on the other ha
Externí odkaz:
https://doaj.org/article/329a53ff7b3f4b3b9d1afd7121e4696e
Publikováno v:
GLOBECOM 2022 - 2022 IEEE Global Communications Conference.
Autor:
Hasin Us Sami, Basak Guler
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Publikováno v:
IEEE Journal on Selected Areas in Information Theory. 2:441-451
How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, whi
Publikováno v:
IEEE Transactions on Signal and Information Processing over Networks. 6:508-525
This article proposes a coded distributed graph processing framework to alleviate the communication bottleneck in large-scale distributed graph processing. In particular, we propose a topology-aware coded computing (TACC) algorithm that has two novel
Autor:
Aylin Yener, Basak Guler
Publikováno v:
WiOpt
Potential environmental impact of machine learning in large-scale wireless networks is a major challenge for the sustainability of next-generation intelligent systems. Federated learning is a recent framework for communication-efficient training of m
Autor:
Basak Guler, Aylin Yener
Publikováno v:
ISIT
This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks. We consider a distributed learning setup in which a machine learning model is trained over a large number of devices that can h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::12b7593c3bef279bc3de84eec5e18a43
Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A major bottleneck in scaling federated learning to a large number of users
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::470ebb9aa6f26481d3889c6376648323
http://arxiv.org/abs/2002.04156
http://arxiv.org/abs/2002.04156
Secure federated learning is a privacy-preserving framework to improve machine learning models by training over large volumes of data collected by mobile users. This is achieved through an iterative process where, at each iteration, users update a gl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e17855fb17f470d7d4d2836518f55ab6