Deep neural networks (DNNs) have brought significant performance improvements to
various real-life applications. However, a DNN training task commonly requires intensive …

Federated learning is a recently proposed paradigm that presents significant advantages in
privacy-preserving machine learning services. It enables the deep learning applications on …

Federated deep learning has been widely used in various fields. To protect data privacy,
many privacy-preservingapproaches have been designed and implemented in various …

In this paper, we consider a mobile edge computing (MEC) system that multiple users
participate in the federated learning protocol by jointly training a deep neural network (DNN) …

Nowadays, Deep Neural Networks are widely applied to various domains. However,
massive data collection required for deep neural network reveals the potential privacy …

Deep learning has achieved the high-accuracy of state-of-the-art algorithms in long-standing
AI tasks. Due to the obvious privacy issues of deep learning, Google proposes Federal Deep …

With the advance of machine learning technology and especially the explosive growth of big
data, federated learning, which allows multiple participants to jointly train a high-quality …

Deep neural networks have strong capabilities of memorizing the underlying training data,
which can be a serious privacy concern. An effective solution to this problem is to train …

Recently, federated learning has gained increasing attention for privacy-preserving
computation since the learning paradigm allows to train models without the need for …

Due to the powerful representation ability and superior performance of Deep Neural
Networks (DNN), Federated Learning (FL) based on DNN has attracted much attention from …