Embedded federated learning for vanet environments
R Valente, C Senna, P Rito, S Sargento - Applied Sciences, 2023 - mdpi.com
In the scope of smart cities, the sensors scattered throughout the city generate information
that supplies intelligence mechanisms to learn the city's mobility patterns. These patterns are …
that supplies intelligence mechanisms to learn the city's mobility patterns. These patterns are …
Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey
With rapidly increasing distributed deep learning workloads in large-scale data centers,
efficient distributed deep learning framework strategies for resource allocation and workload …
efficient distributed deep learning framework strategies for resource allocation and workload …
Fast and scalable all-optical network architecture for distributed deep learning
With the ever-increasing size of training models and datasets, network communication has
emerged as a major bottleneck in distributed deep learning training. To address this …
emerged as a major bottleneck in distributed deep learning training. To address this …
Scalable fully pipelined hardware architecture for in-network aggregated AllReduce communication
The Ring-AllReduce framework is currently the most popular solution to deploy industry-
level distributed machine learning tasks. However, only about half of the maximum …
level distributed machine learning tasks. However, only about half of the maximum …
BaPipe: Exploration of balanced pipeline parallelism for DNN training
The size of deep neural networks (DNNs) grows rapidly as the complexity of the machine
learning algorithm increases. To satisfy the requirement of computation and memory of DNN …
learning algorithm increases. To satisfy the requirement of computation and memory of DNN …
Convergence Analysis of Decentralized ASGD
MDL Tosi, M Theobald - arXiv preprint arXiv:2309.03754, 2023 - arxiv.org
Over the last decades, Stochastic Gradient Descent (SGD) has been intensively studied by
the Machine Learning community. Despite its versatility and excellent performance, the …
the Machine Learning community. Despite its versatility and excellent performance, the …
Distributed artificial intelligence: review, taxonomy, framework, and reference architecture
Artificial intelligence (AI) research and market have grown rapidly in the last few years and
this trend is expected to continue with many potential advancements and innovations in this …
this trend is expected to continue with many potential advancements and innovations in this …
[图书][B] High Performance Computing in Clouds: Moving HPC Applications to a Scalable and Cost-Effective Environment
This book brings a thorough explanation on the path needed to use cloud computing
technologies to run High-Performance Computing (HPC) applications. Besides presenting …
technologies to run High-Performance Computing (HPC) applications. Besides presenting …
SaPus: Self-adaptive parameter update strategy for DNN training on Multi-GPU clusters
Parameter server architecture has been identified as an efficient framework for scaling
DNNs training on clusters. For large-scale deployment, communication becomes the …
DNNs training on clusters. For large-scale deployment, communication becomes the …
Using VDMS to index and search 100M images
L Remis, CW Lacewell - Proceedings of the VLDB Endowment, 2021 - dl.acm.org
Data scientists spend most of their time dealing with data preparation, rather than doing
what they know best: build machine learning models and algorithms to solve previously …
what they know best: build machine learning models and algorithms to solve previously …