More and more Internet of Things (IoT) applications provide intelligent services, with the
development of artificial intelligence algorithms, such as deep reinforcement learning …

Although Deep Neural Networks (DNN) have become the backbone technology of several
ubiquitous applications, their deployment in resource-constrained machines, eg, Internet of …

Federated learning (FL) has evolved to leverage a distributed dataset from numerous IoT
devices to improve the performance of a Machine Learning (ML) model while preserving the …

Edge computing is a distributed computing paradigm that involves processing data at or
near the edge of the internet of things (IoT) network, instead of centralized server. This …

Edge computing and Federated Learning (FL) can work in tandem to address issues related
to privacy and collaborative distributed learning in untrusted IoT environments. However …

Federated learning (FL) has emerged to leverage datasets from multiple devices to improve
the performance of a machine learning (ML) model while providing privacy preservation for …

Attacks against the IoT network are increasing rapidly, leading to an exponential growth in
the number of unsecured IoT devices. Existing security mechanisms are facing several …

Along with the proliferation of the Internet of Things (IoT) and the surge in the use of artificial
intelligence (AI), Edge Computing has proved considerable success in reducing latency …

The increasing number of Internet of Things (IoT) devices motivate the data sharing that
improves the quality of IoT services. However, data providers usually suffer from the privacy …

With the explosive growth of data generated by the Internet of Things (IoT) devices, the
traditional cloud computing model by transferring all data to the cloud for processing has …