Federated Learning and Privacy
Federated Learning is a distributed learning paradigm which arises from both, the increasing awareness of data privacy and the growing number of smart devices connected to the internet. To sum up, Federated Learning consists of a set of clients which agree to train a learning model in a collaborative way under the orchestration of a central server. Although the learning process is orchestrated by a central server, the training data of each client is never shared so it increases their privacy. This is the main difference with the classic distributed learning setup.
Even though, client’s training data is not shared, they share their trained models and there are several ways to extract additional information from such models. To ensure a higher degree of privacy many Differential Privacy techniques can be employed.
There are many open problems and challenges in this field, such as: adversarial attacks and defense mechanisms, personalization, adapting classic machine learning models to this federated setup and so on.
Contact: María Victoria Luzón García
Related Researchers:
Letra: |
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Herrera Triguero, Francisco | herrera@dejUCsMr_Hcsai.ugr.es | DaSCI Technology Applications Area, Data Science and Big Data Area, Computational Intelligence Area | PhD |
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Luzón García, María Victoria | luzon@ugr.aDuKZElkkes | DaSCI Technology Applications Area | PhD |
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Martínez Cámara, Eugenio | emcamara@dN20YAYecsai.ugr.es | DaSCI Technology Applications Area | PhD - Juan de la Cierva |
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Peregrin Rubio, Antonio | peregrin@dti.uhu3i36Q.s.es | Computational Intelligence Area | PhD |
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Peregrin Rubio, Antonio | peregrin@dti.uhu8mROlgUjYi3.es | Computational Intelligence Area | PhD |