Environmental selection is an important process in multi-objective evolutionary algorithms (MOEAs). As the evolution progresses, the number of non-dominated solutions increases. This paper is focused on selecting a su...
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It is a novel but challenging task that simulated desert crossing strategy for the game players under the specified conditions. This study is aimed to develop an optimal action strategy approach for desert crossing ba...
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Dear editor,How to deal with uncertainties and/or disturbances is a central issue pushing the development of both controlscience and controltechnology. Among various approaches, the active disturbance rejection cont...
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Dear editor,How to deal with uncertainties and/or disturbances is a central issue pushing the development of both controlscience and controltechnology. Among various approaches, the active disturbance rejection control (ADRC) has been successfully implemented in various industrial practices because of its uniqueness in concepts, simplicity
Source term estimation (STE) of hazardous gas leakages in chemical industrial parks (CIPs) is important for addressing environmental pollution and improving engineering safety and reliability. For this purpose, some l...
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Electroencephalogram (EEG) is a nonlinear signal that reflects the physio-logical state of the brain at different times, containing rich information. However, the possible interference during the collection and transm...
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Traditional radar signal modulation recognition (RSMR) methods struggle to achieve the required accuracy under low signal-to-noise ratio (SNR) conditions. To address this issue, a hybrid network architecture integrati...
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Convolutional neural networks (CNN) have shown excellent capability in synthetic aperture radar (SAR) target recognition. However, obtaining sufficient training samples for improving network recognition is difficult d...
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The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptiv...
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Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) bas...
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Machine learning is a key component of many applications, among which decentralized learning has attracted wide attention because of its cost-effectiveness and high efficiency. However, decentralized learning is vulne...
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ISBN:
(数字)9798350379228
ISBN:
(纸本)9798350390780
Machine learning is a key component of many applications, among which decentralized learning has attracted wide attention because of its cost-effectiveness and high efficiency. However, decentralized learning is vulnerable to various Byzantine attacks. Existing defense models face challenges in defending against complex attacks and are often constrained by strict network topologies. To address this issue, a parameter aggregation rule based on reputation evaluation (RPV) is proposed in this paper. This rule establishes a reputation model for neighboring nodes and continuously updates it based on their distance performance during each iteration. By applying the reputation model of neighboring nodes to the Beta distribution, their reliability is obtained. Based on the reliability of neighboring nodes, the set of reliable nodes for this iteration and select parameter values from them for aggregation are determined. Finally, we gradually eliminate the influence of Byzantine nodes. Our experimental results on the MNIST dataset demonstrate that the proposed algorithm is resilient to attacks from any number of Byzantine nodes and outperforms previous defense models in terms of network topology constraints, training accuracy, and computational costs.
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