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pro vyhledávání: '"Guirguis, Arsany Hany Abdelmessih"'
Machine learning (ML) applications are ubiquitous. They run in different environments such as datacenters, the cloud, and even on edge devices. Despite where they run, distributing ML training seems the only way to attain scalable, high-quality learn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71ced68bb8ecc89366aaeb75a4ef25b5
Autor:
Damaskinos, Georgios, El Mhamdi, El Mahdi, Guerraoui, Rachid, Guirguis, Arsany Hany Abdelmessih, Rouault, Sébastien Louis Alexandre
We present AGGREGATHOR, a framework that implements state-of-the-art robust (Byzantine-resilient) distributed stochastic gradient descent. Following the standard parameter server model, we assume that a minority of worker machines can be controlled b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______185::803f930c8856ba090afd07ad7f6f9740
https://infoscience.epfl.ch/record/265684
https://infoscience.epfl.ch/record/265684