Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Ács, Gergely"'
Federated learning has become a widely used paradigm for collaboratively training a common model among different participants with the help of a central server that coordinates the training. Although only the model parameters or other model updates a
Externí odkaz:
http://arxiv.org/abs/2303.03908
Autor:
Oldenhof, Martijn, Ács, Gergely, Pejó, Balázs, Schuffenhauer, Ansgar, Holway, Nicholas, Sturm, Noé, Dieckmann, Arne, Fortmeier, Oliver, Boniface, Eric, Mayer, Clément, Gohier, Arnaud, Schmidtke, Peter, Niwayama, Ritsuya, Kopecky, Dieter, Mervin, Lewis, Rathi, Prakash Chandra, Friedrich, Lukas, Formanek, András, Antal, Peter, Rahaman, Jordon, Zalewski, Adam, Heyndrickx, Wouter, Oluoch, Ezron, Stößel, Manuel, Vančo, Michal, Endico, David, Gelus, Fabien, de Boisfossé, Thaïs, Darbier, Adrien, Nicollet, Ashley, Blottière, Matthieu, Telenczuk, Maria, Nguyen, Van Tien, Martinez, Thibaud, Boillet, Camille, Moutet, Kelvin, Picosson, Alexandre, Gasser, Aurélien, Djafar, Inal, Simon, Antoine, Arany, Ádám, Simm, Jaak, Moreau, Yves, Engkvist, Ola, Ceulemans, Hugo, Marini, Camille, Galtier, Mathieu
To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research
Externí odkaz:
http://arxiv.org/abs/2210.08871
Pharmaceutical industry can better leverage its data assets to virtualize drug discovery through a collaborative machine learning platform. On the other hand, there are non-negligible risks stemming from the unintended leakage of participants' traini
Externí odkaz:
http://arxiv.org/abs/2205.06506
Federated learning becomes a prominent approach when different entities want to learn collaboratively a common model without sharing their training data. However, Federated learning has two main drawbacks. First, it is quite bandwidth inefficient as
Externí odkaz:
http://arxiv.org/abs/2103.00342
Federated Learning allows distributed entities to train a common model collaboratively without sharing their own data. Although it prevents data collection and aggregation by exchanging only parameter updates, it remains vulnerable to various inferen
Externí odkaz:
http://arxiv.org/abs/2011.05578
Federated Learning enables entities to collaboratively learn a shared prediction model while keeping their training data locally. It prevents data collection and aggregation and, therefore, mitigates the associated privacy risks. However, it still re
Externí odkaz:
http://arxiv.org/abs/2010.07808
The unavailability of training data is a permanent source of much frustration in research, especially when it is due to privacy concerns. This is particularly true for location data since previous techniques all suffer from the inherent sparseness an
Externí odkaz:
http://arxiv.org/abs/2008.01665
Data generated by cars is growing at an unprecedented scale. As cars gradually become part of the Internet of Things (IoT) ecosystem, several stakeholders discover the value of in-vehicle network logs containing the measurements of the multitude of s
Externí odkaz:
http://arxiv.org/abs/1911.09508
Data is the new oil for the car industry. Cars generate data about how they are used and who's behind the wheel which gives rise to a novel way of profiling individuals. Several prior works have successfully demonstrated the feasibility of driver re-
Externí odkaz:
http://arxiv.org/abs/1902.08956
Autor:
Gazdag, András, Lestyán, Szilvia, Remeli, Mina, Ács, Gergely, Holczer, Tamás, Biczók, Gergely
Publikováno v:
In Vehicular Communications February 2023 39