A Federated Learning Framework for Privacy-preserving and Parallel Training

22 Jan 2020 Tien-Dung Cao Tram Truong-Huu Hien Tran Khanh Tran

The deployment of such deep learning in practice has been hurdled by two issues: the computational cost of model training and the privacy issue of training data such as medical or healthcare records. The large size of both learning models and datasets incurs a massive computational cost, requiring efficient approaches to speed up the training phase... (read more)

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