Dynamic and adaptive fault-tolerant asynchronous federated learning using volunteer edge devices

Morell, José Ángel; Alba, Enrique

doi:10.1016/j.future.2022.02.024

Cited by 16 publications

(6 citation statements)

References 25 publications

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“…The algorithm is based on the stochastic gradient descent algorithm improvement, which initializes the weights and distributes them to each device or computing node for training through a central server. This method optimizes model training to improve the efficiency and accuracy of data processing while also protecting privacy [17][18][19] . After each iteration, a certain proportion of participants will be selected from the iteration results for optimization.…”

Section: Data Imbalance Processing On the Grounds Of Fedavgmentioning

confidence: 99%

Privacy Protection Based on Federated Learning

Liu,

Eric B. Blancaflor,

Fang

et al. 2024

JAIT

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With the development of artificial intelligence technology, more and more fields will collect relevant user data, and provide users with a better experience through data analysis. But there are also risks involved in the process of data collection, namely how to protect personal privacy data. To address this issue, this study combined differential privacy, convolutional neural networks, and federated averaging algorithms to construct a privacy protection model. The study first utilized the federated average algorithm to handle data imbalance, ensuring that each analyzed data is in a balanced state. Then, based on of data balancing, a new algorithm model was constructed using differential privacy and convolutional neural networks. Finally, it utilized a number of public datasets to verify the role of the model in privacy protection. The results showed that the model can achieve recognition accuracy of 97.27% and 93.15%, respectively , for data under the influence of privacy budget and relaxation factor. Meanwhile, the classification accuracy of the model for data can reached 95.31%, with a regression error of 9.03%. When the local iteration number of the device was 30, the testing accuracy can reached 95.28%. This indicates that methods on the grounds of federated averaging algorithm and differential privacy can maintain the accuracy of the model while protecting user privacy. The application research of models has strong practical significance.

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Section: Data Imbalance Processing On the Grounds Of Fedavgmentioning

confidence: 99%

Privacy Protection Based on Federated Learning

Liu,

Eric B. Blancaflor,

Fang

et al. 2024

JAIT

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“…Therefore, potential faults can be predicted based on the characteristics of tasks executed in the long term. Resources should be provided to address the faults, reducing the energy consumption [139]. Thereby, prediction for errors in tasks is also a future research direction of service fault-tolerant scheduling.…”

Section: Opportunities In Service Faulttolerant Schedulingmentioning

confidence: 99%

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

Luo¹,

Li²,

Yang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and outline future directions in this area. In particular, we divide intelligent identification over power big data into data acquisition and storage processes, anomaly detection and fault discrimination processes, and fault tracing for dispatching operations during communication. A detailed survey of the solutions to the challenges in intelligent identification over power big data is then presented. Moreover, opportunities and future directions are outlined.

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“…TensorFlow.js is used as a proof-of-concept to train a recurrent neural network that produces or predicts the following letter of an input text. In a follow-up study [ 83 ], a federated learning system that is dynamic and adaptable has been implemented and evaluated to train common machine learning models. The system has been evaluated with up to 24 desktops working together via web browsers to train common machine learning models, while allowing users to join or leave the computation and keeping personal data stored locally.…”

Section: Front-end Deep Learning Web Appsmentioning

confidence: 99%

Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

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Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification.

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Dynamic and adaptive fault-tolerant asynchronous federated learning using volunteer edge devices

Cited by 16 publications

References 25 publications

Privacy Protection Based on Federated Learning

Privacy Protection Based on Federated Learning

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

Front-end deep learning web apps development and deployment: a review

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