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pro vyhledávání: '"Tenison, Irene"'
Autor:
Tenison, Irene
L'apprentissage fédéré est un paradigme émergent qui permet à un grand nombre de clients disposant de données hétérogènes de coordonner l'apprentissage d'un modèle global unifié sans avoir besoin de partager les données entre eux ou avec
Externí odkaz:
http://hdl.handle.net/1866/27954
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data. This paves the way for stronger privacy guarantees when building predictive models. The most used algorithms for FL are par
Externí odkaz:
http://arxiv.org/abs/2211.04742
Autor:
Tenison, Irene, Sreeramadas, Sai Aravind, Mugunthan, Vaikkunth, Oyallon, Edouard, Rish, Irina, Belilovsky, Eugene
Federated learning (FL) is an emerging paradigm that permits a large number of clients with heterogeneous data to coordinate learning of a unified global model without the need to share data amongst each other. A major challenge in federated learning
Externí odkaz:
http://arxiv.org/abs/2201.11986
Publikováno v:
ICLR 2021 Distributed and Private Machine Learning(DPML) Workshop
Federated Averaging (FedAVG) has become the most popular federated learning algorithm due to its simplicity and low communication overhead. We use simple examples to show that FedAVG has the tendency to sew together the optima across the participatin
Externí odkaz:
http://arxiv.org/abs/2104.10322
Publikováno v:
ICLR 2021 Distributed and Private Machine Learning(DPML) Workshop
Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating devices (clients) and aggregating the local models into a global one. As
Externí odkaz:
http://arxiv.org/abs/2104.06557