Fog-cloud computing promises many new vertical service areas beyond simple data
communication, storing, and processing. Among them, distributed deep learning (DDL) …

As the extension of cloud computing and a foundation of IoT, fog computing is experiencing
fast prosperity because of its potential to mitigate some troublesome issues, such as network …

Federated learning (FL) has been widely applied in Internet of Things (IoT). However, two
security problems hinder the proliferation of FL in practical IoT, ie, privacy leakage and …

In current deep learning models, centralized architecture forces participants to pool their
data to the central Cloud to train a global model, while distributed architecture requires a …

Federated learning (FL), as a prospective way to process and analyze the massive data from
the Internet of Things (IoT) devices, has attracted increasing attention from academia and …

In state-of-the-art deep learning, centralized deep learning forces end devices to pool their
data in the cloud in order to train a global model on the joint data, while distributed deep …

Distributed deep learning (DDL) naturally provides a privacy-preserving solution to enable
multiple parties to jointly learn a deep model without explicitly sharing the local datasets …

Federated learning can combine a large number of scattered user groups and train models
collaboratively without uploading data sets, so as to avoid the server collecting user …

Fog platform brings the computing power from the remote cloud-side closer to the edge
devices to reduce latency, as the unprecedented generation of data causes ineligible …

Cloud computing is now a popular computing paradigm that can provide end users access
to configurable resources on any device, from anywhere, at any time. During the past years …