Distributed Computing
68 papers with code • 0 benchmarks • 1 datasets
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Latest papers with no code
The intelligent prediction and assessment of financial information risk in the cloud computing model
Cloud computing (cloud computing) is a kind of distributed computing, referring to the network "cloud" will be a huge data calculation and processing program into countless small programs, and then, through the system composed of multiple servers to process and analyze these small programs to get the results and return to the user.
Going Forward-Forward in Distributed Deep Learning
This paper introduces a new approach in distributed deep learning, utilizing Geoffrey Hinton's Forward-Forward (FF) algorithm to enhance the training of neural networks in distributed computing environments.
Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning
While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning
Task offloading, crucial for balancing computational loads across devices in networks such as the Internet of Things, poses significant optimization challenges, including minimizing latency and energy usage under strict communication and storage constraints.
Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning
This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning.
A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades.
MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning
As a new distributed computing framework that can protect data privacy, federated learning (FL) has attracted more and more attention in recent years.
Computational Offloading in Semantic-Aware Cloud-Edge-End Collaborative Networks
To minimize long-term energy consumption on constraints queue stability and computational delay, a Lyapunov-guided deep reinforcement learning hybrid (DRLH) framework is proposed to solve the mixed integer non-linear programming (MINLP) problem.
Multiple Access in the Era of Distributed Computing and Edge Intelligence
This paper focuses on the latest research and innovations in fundamental next-generation multiple access (NGMA) techniques and the coexistence with other key technologies for the sixth generation (6G) of wireless networks.
Consensus learning: A novel decentralised ensemble learning paradigm
This work introduces a novel distributed machine learning paradigm -- \emph{consensus learning} -- which combines classical ensemble methods with consensus protocols deployed in peer-to-peer systems.
