Self-aware distributed deep learning framework for heterogeneous IoT edge devices

TL;DR: In this article , the authors provide an architecture for enabling AI in fully edge-based scenarios and provide strategies to tackle the communication inefficiencies that arise from the distributed nature of fully edgebased scenarios.

TL;DR: In this article , a deep learning-based human activity recognition (HAR) model, called MultiCNN-FilterLSTM, was proposed, which combines a multi-head convolutional neural network (CNN) with a long-short-term memory (LR) through a residual connection.

TL;DR: Experimental results demonstrate that the proposed design can collaboratively perform training tasks with optimized efficiency and provide dependable collaborations for system fault detection and cluster extension.

TL;DR: The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.

TL;DR: This work introduces a framework, FedProx, to tackle heterogeneity in federated networks, and provides convergence guarantees for this framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work.

TL;DR: Docker promises the ability to package applications and their dependencies into lightweight containers that move easily between different distros, start up quickly and are isolated from each other.

TL;DR: This paper presents a practical system that enables multiple parties to jointly learn an accurate neural-network model for a given objective without sharing their input datasets, and exploits the fact that the optimization algorithms used in modern deep learning, namely, those based on stochastic gradient descent, can be parallelized and executed asynchronously.