Edge computing is a key-enabling technology that meets continuously increasing
requirements for the intelligent Internet-of-Things (IoT) applications. To cope with the …

Federated learning is a novel distributed learning framework, which enables thousands of
participants to collaboratively construct a deep learning model. In order to protect …

Federated learning can complete an enormous training task efficiently by inviting
participants to train a deep learning model collaboratively, and the user privacy will be well …

Training a deep neural network requires substantial data and intensive computing
resources. Unaffordable price holds back many potential applications of deep learning …

Federated learning is a novel distributed learning framework, where the deep learning
model is trained in a collaborative manner among thousands of participants. The shares …

Federated learning is inherently vulnerable to poisoning attacks, since no training samples
will be released to and checked by trustworthy authority. Poisoning attacks are widely …

Federated machine learning which enables resource-constrained node devices (eg, Internet
of Things (IoT) devices and smartphones) to establish a knowledge-shared model while …

We propose a model-based reinforcement learning framework to derive untargeted
poisoning attacks against federated learning (FL) systems. Our framework first approximates …

Federated learning is a privacy-by-design framework that enables training deep neural
networks from decentralized sources of data, but it is fraught with innumerable attack …

Federated learning is usually utilized as a fraud detection framework in the domain of
financial risk management, which promotes the model accuracy without training data …