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pro vyhledávání: '"Pethick, Thomas Michaelsen"'
Autor:
Ramezani-Kebrya, Ali, Liu, Fanghui, Pethick, Thomas Michaelsen, Chrysos, Grigorios, Cevher, Volkan
This paper addresses intra-client and inter-client covariate shifts in federated learning (FL) with a focus on the overall generalization performance. To handle covariate shifts, we formulate a new global model training paradigm and propose Federated
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ebd72986e1a493653ef8000752893d85
Autor:
Latorre, Fabian, Krawczuk, Igor, Dadi, Leello Tadesse, Pethick, Thomas Michaelsen, Cevher, Volkan
Adversarial Training using a strong first-order adversary (PGD) is the gold standard for training Deep Neural Networks that are robust to adversarial examples. We show that, contrary to the general understanding of the method, the gradient at an opti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______185::8ae59200751cd5387c48e72942ae4a51
https://infoscience.epfl.ch/record/300850
https://infoscience.epfl.ch/record/300850