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

Edge Computing (EC) has seen a continuous rise in its popularity as it provides a solution to
the latency and communication issues associated with edge devices transferring data to …

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

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

With the massive amounts of data generated by industrial Internet of Things (IIoT) devices at
all moments, federated learning (FL) enables these distributed distrusted devices to …

Data poisoning attack is one of the common attacks that decreases the performance of a
model in edge machine learning. The mechanism used in most of the existing data …

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

While recent works have indicated that federated learning (FL) may be vulnerable to
poisoning attacks by compromised clients, their real impact on production FL systems is not …

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

Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep
neural networks in which participants' data remains on their own devices with only model …