Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey
Nowadays, many research studies and industrial investigations have allowed the integration
of the Internet of Things (IoT) in current and future networking applications by deploying a …
of the Internet of Things (IoT) in current and future networking applications by deploying a …
Federated learning for edge networks: Resource optimization and incentive mechanism
Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices.
IoT devices with intelligence require the use of effective machine learning paradigms …
IoT devices with intelligence require the use of effective machine learning paradigms …
A survey on deep learning empowered IoT applications
The Internet of Things (IoT) is widely regarded as a key component of the Internet of the
future and thereby has drawn significant interests in recent years. IoT consists of billions of …
future and thereby has drawn significant interests in recent years. IoT consists of billions of …
Enabling compute-communication overlap in distributed deep learning training platforms
Deep Learning (DL) training platforms are built by interconnecting multiple DL accelerators
(eg, GPU/TPU) via fast, customized interconnects with 100s of gigabytes (GBs) of bandwidth …
(eg, GPU/TPU) via fast, customized interconnects with 100s of gigabytes (GBs) of bandwidth …
Partitioning convolutional neural networks to maximize the inference rate on constrained IoT devices
F Martins Campos de Oliveira, E Borin - Future Internet, 2019 - mdpi.com
Billions of devices will compose the IoT system in the next few years, generating a huge
amount of data. We can use fog computing to process these data, considering that there is …
amount of data. We can use fog computing to process these data, considering that there is …
Distributed learning in wireless networks: Recent progress and future challenges
The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …
applications to efficiently analyze various types of data collected by edge devices for …
Learning how to communicate in the Internet of Things: Finite resources and heterogeneity
For a seamless deployment of the Internet of Things (IoT), there is a need for self-organizing
solutions to overcome key IoT challenges that include data processing, resource …
solutions to overcome key IoT challenges that include data processing, resource …
Communication-efficient distributed AI strategies for the IoT edge
The impact that artificial intelligence (AI) has made across several industries in today's
society is clearly seen in applications ranging from medical diagnosis to customer service …
society is clearly seen in applications ranging from medical diagnosis to customer service …
Low-latency federated learning with DNN partition in distributed industrial IoT networks
Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed
intelligence of industrial automation thanks to its capability of distributed machine learning …
intelligence of industrial automation thanks to its capability of distributed machine learning …
V-edge: Virtual edge computing as an enabler for novel microservices and cooperative computing
As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its
core idea is still intriguing: Instead of sending all data and tasks from an end user's device to …
core idea is still intriguing: Instead of sending all data and tasks from an end user's device to …