Attention-Based Distributed Deep Learning Model for Air Quality Forecasting

An Efficient Matching Game Approach to Association Formation in UAV-Enabled Hierarchical Distributed Learning

Distributed machine learning has emerged as a promising data processing technology for next-generation communication systems. It leverages the computational capabilities of local nodes to efficiently handle large datasets, creating highly accurate data-driven models for analysis and prediction purposes. However, the performance of distributed machine learning can be significantly hampered by communication bottlenecks and node dropouts. In this article, a novel unmanned aerial vehicle (UAV)-enabled hierarchical distributed learning architecture is proposed to support machine learning applications, e.g., regional monitoring. Multiple UAV receivers (URs) are introduced as wireless relays to improve the communication between the UAV transmitters (UTs) and the cloud server. Our objective is to identify the optimal UT-UR association to maximize the social welfare of the network, which is distinctly different from the existing works that focus on the unilateral profit-maximizing problem. We formulate a two-side many-to-one matching game to model the UT-UR association problem, and a two-phase many-to-one matching algorithm is designed to identify the stable matching. The validity of our proposed scheme is verified through in-depth numerical simulations.

Deep-learning architecture for PM2.5 concentration prediction: A review

Accurately predicting the concentration of fine particulate matter (PM2.5) is crucial for evaluating air pollution levels and public exposure. Recent advancements have seen a significant rise in using deep learning (DL) models for forecasting PM2.5 concentrations. Nonetheless, there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM2.5 prediction models. Here we extensively reviewed those DL-based hybrid models for forecasting PM2.5 levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examined the similarities and differences among various DL models in predicting PM2.5 by comparing their complexity and effectiveness. We categorized PM2.5 DL methodologies into seven types based on performance and application conditions, including four types of DL-based models and three types of hybrid learning models. Our research indicates that established deep learning architectures are commonly used and respected for their efficiency. However, many of these models often fall short in terms of innovation and interpretability. Conversely, models hybrid with traditional approaches, like deterministic and statistical models, exhibit high interpretability but compromise on accuracy and speed. Besides, hybrid DL models, representing the pinnacle of innovation among the studied models, encounter issues with interpretability. We introduce a novel three-dimensional evaluation framework, i.e., Dataset-Method-Experiment Standard (DMES) to unify and standardize the evaluation for PM2.5 predictions using DL models. This review provides a framework for future evaluations of DL-based models, which could inspire researchers to standardize DL model usage in PM2.5 prediction and improve the quality of related studies.

Comparative Study on Distributed Lightweight Deep Learning Models for Road Pothole Detection

This paper delves into image detection based on distributed deep-learning techniques for intelligent traffic systems or self-driving cars. The accuracy and precision of neural networks deployed on edge devices (e.g., CCTV (closed-circuit television) for road surveillance) with small datasets may be compromised, leading to the misjudgment of targets. To address this challenge, TensorFlow and PyTorch were used to initialize various distributed model parallel and data parallel techniques. Despite the success of these techniques, communication constraints were observed along with certain speed issues. As a result, a hybrid pipeline was proposed, combining both dataset and model distribution through an all-reduced algorithm and NVlinks to prevent miscommunication among gradients. The proposed approach was tested on both an edge cluster and Google cluster environment, demonstrating superior performance compared to other test settings, with the quality of the bounding box detection system meeting expectations with increased reliability. Performance metrics, including total training time, images/second, cross-entropy loss, and total loss against the number of the epoch, were evaluated, revealing a robust competition between TensorFlow and PyTorch. The PyTorch environment's hybrid pipeline outperformed other test settings.