Energy-efficient Training of Distributed DNNs in the Mobile-edge-cloud Continuum
F Malandrino, CF Chiasserini… - 2022 17th Wireless On …, 2022 - ieeexplore.ieee.org
We address distributed machine learning in multitier (eg, mobile-edge-cloud) networks
where a heterogeneous set of nodes cooperate to perform a learning task. Due to the …
where a heterogeneous set of nodes cooperate to perform a learning task. Due to the …
Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network
H Wu, P Wang, CV Narayana - arXiv preprint arXiv:2311.10002, 2023 - arxiv.org
Federated Learning (FL) enables many resource-limited devices to train a model
collaboratively without data sharing. However, many existing works focus on model …
collaboratively without data sharing. However, many existing works focus on model …
Fully distributed deep learning inference on resource-constrained edge devices
Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
privacy of input data and can result in shorter latency when compared to a cloud solution. As …
A framework for distributed deep neural network training with heterogeneous computing platforms
Deep neural network (DNN) training is generally performed by cloud computing platforms.
However, cloud-based training has several problems such as network bottleneck, server …
However, cloud-based training has several problems such as network bottleneck, server …
Edge–IoT computing and networking resource allocation for decomposable deep learning inference
Deep learning (DL) applications have attracted significant attention with the rapidly growing
demand for Internet of Things (IoT) systems. However, performing the inference tasks for DL …
demand for Internet of Things (IoT) systems. However, performing the inference tasks for DL …
EEAI: An End-edge Architecture for Accelerating Deep Neural Network Inference
Deep Neural Networks (DNNs), as a key technology for Artificial Intelligence (AI)
applications in the 5G era, have been widely used in the field of mobile intelligence …
applications in the 5G era, have been widely used in the field of mobile intelligence …
End-Edge Collaborative Inference of Convolutional Fuzzy Neural Networks for Big Data-Driven Internet of Things
Deep Neural Networks (DNN) has been widely applied in big data-driven Internet of Things
(IoT) for excellent learning ability, while the black-box nature of DNN leads to uncertainty of …
(IoT) for excellent learning ability, while the black-box nature of DNN leads to uncertainty of …
Accelerate cooperative deep inference via layer-wise processing schedule optimization
Computation offloading is proposed to solve one obstacle of enabling high-accurate and
real-time deep inference in resource-constrained Internet of Things (IoT) devices …
real-time deep inference in resource-constrained Internet of Things (IoT) devices …
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 …
Optimizing CNN Inference Speed over Big Social Data through Efficient Model Parallelism for Sustainable Web of Things
The rapid development of artificial intelligence and networking technologies has catalyzed
the popularity of intelligent services based on deep learning in recent years, which in turn …
the popularity of intelligent services based on deep learning in recent years, which in turn …