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A dynamic queuing model based distributed task offloading algorithm using deep reinforcement learning in mobile edge computing
In mobile edge computing (MEC), offloading computing tasks from edge clients to edge nodes can reduce the burden on edge clients, especially for...
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Joint Task Offloading Based on Distributed Deep Reinforcement Learning-Based Genetic Optimization Algorithm for Internet of Vehicles
The growing number of individual vehicles and intelligent transportation systems have accelerated the development of Internet of Vehicles (IoV)...
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Out-of-the-box parameter control for evolutionary and swarm-based algorithms with distributed reinforcement learning
Parameter control methods for metaheuristics with reinforcement learning put forward so far usually present the following shortcomings: (1) Their...
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Privacy-preserving collaborative AI for distributed deep learning with cross-sectional data
Recent progress in Deep Learning (DL) has shown potential in intelligent healthcare applications, enhancing patients’ quality of life. However,...
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Unleashing potentials with deep learning: decoding the complex events for distributed fiber optic sensing applications
Addressing the classification performance challenge in Φ-OTDR real-world applications due to the difficulty in obtaining enough labeled samples, we...
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Distributed Analysis Dictionary Learning Using a Diffusion Strategy
We consider the problem of distributed dictionary learning which aims to learn a global dictionary from data geographically distributed on nodes of a...
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Privacy-Preserving and Reliable Distributed Federated Learning
Federated learning enables collaborative training of the global model by participants with diverse data sources while preserving data privacy....
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Distributed Backdoor Attacks in Federated Learning Generated by DynamicTriggers
The emergence of federated learning has alleviated the dual challenges of data silos and data privacy and security in machine learning. However, this...
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A distributed learning based sentiment analysis methods with Web applications
The main challenge of using deep learning (DL) for sentiment analysis tasks is that insufficient data leads to a decrement in classification...
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Reliable adaptive distributed hyperparameter optimization (RadHPO) for deep learning training and uncertainty estimation
Training and validation of Neural Networks (NN) are very computationally intensive. In this paper, we propose a distributed system based NN...
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MP-DPS: adaptive distributed training for deep learning based on node merging and path prediction
With the increasing scale of data sets and neural network models, distributed training of deep neural networks has become a trend. The main...
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Few-Shot and Transfer Learning with Manifold Distributed Datasets
A manifold distributed dataset with limited labels makes it difficult to train a high-mean accuracy classifier. Transfer learning is beneficial in...
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Distributed localization for IoT with multi-agent reinforcement learning
Localization has become one of the important techniques for Internet of Things (IoT). However, most existing localization methods need a central...
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Big-IDS: a decentralized multi agent reinforcement learning approach for distributed intrusion detection in big data networks
The growing complexity of security threats and the pervasive prevalence of cyberattacks have become more apparent in the present era, and the advent...
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EP4DDL: addressing straggler problem in heterogeneous distributed deep learning
Driven by big data, neural networks evolve more complex and the computing capacity of a single machine is often difficult to meet the demand....
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Distributed and Collaborative Learning Approach for Stroke Prediction
In this paper, we focus on solving a binary classification problem for stroke prediction. The proposed approach is based on a decentralized and...
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Non-IID Distributed Learning with Optimal Mixture Weights
Distributed learning can well solve the problem of training model with large-scale data, which has attracted much attention in recent years. However,...
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Research on Distributed Machine Learning Model for Predicting Users’ Interest by Acquired Web Contents Similarity
This paper discusses and proposes a method for predicting and analyzing the current user interests based on their characteristics including their own...
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CF-DAML: Distributed automated machine learning based on collaborative filtering
The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data...
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Federated Learning for Collaborative Cybersecurity of Distributed Healthcare
Healthcare 4.0 is a new paradigm for providing healthcare services in highly distributed and complex settings. The distributed and heterogeneous...