Orateur
Xuan Vinh Doan
(The University of Warwick)
Description
Federated learning concerns training global models in a decentralized manner. Federated learning is important in many applications, especially when training data come from different sources that cannot be shared with the central server due to restrictions on data sharing. With the increasing capacity of data sources, training samples are usually collected and stored on a regular basis, which leads to an additional issue of sample selection from data sources when one needs to train a global model using the federated learning framework. In this talk, we propose a robust optimization model to incorporate sample selection into global models and develop the robust federated learning framework to train such models.
Author
Xuan Vinh Doan
(The University of Warwick)
Co-auteurs
Dr
Danh Le Phuoc
(Technische Universität Berlin)
M.
Manh Nguyen Duc
(Technische Universität Berlin)
