Federated learning is a new type of privacy-preserving distributed machinelearning method. Despite its advantages, Federated learning suffers from catastrophic forgetting due to the temporal variability of data on pa...
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ISBN:
(纸本)9789819756742;9789819756759
Federated learning is a new type of privacy-preserving distributed machinelearning method. Despite its advantages, Federated learning suffers from catastrophic forgetting due to the temporal variability of data on participating devices. While some methods have been proposed to address this issue in federated learning, they often come at the expense of storage space, which is not a practical solution. This paper presents a federated incremental learning framework designed specifically for resource-constrained devices to address catastrophic forgetting. Leveraging a feature generator, knowledge distillation, and dynamic adaptive weight allocation, the framework addresses catastrophic forgetting and accelerates model convergence. Our framework effectively addresses the issue of catastrophic forgetting, even in the context of resource-constrained devices with limited storage. The results demonstrate significant improvements in our framework compared to existing baselines on the CIFAR-10 and CIFAR-100 datasets.
Handwritten digit recognition is a complex task in various real-world applications such as bank check processing, postal automation recognition etc. In recent time, different kind of learning algorithms are used to an...
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The agricultural study is critically important from a financial perspective. Research areas in agriculture are demand prediction, crop yielding prediction, soil classification, diseases, and weed detection Another dim...
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The modern sedentary lifestyle has escalated health risks, underscoring the importance of tailored health solutions like yoga. This paper pioneers a method to determine yoga'srelevance for individuals by analyzing...
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Credit Card Fraud Detection is one of the vital issues nowadays which needs to be tackled urgently. In today's world, everyone is shifting to an online and cashless world for easiness in the transaction. However, ...
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Fake image detection has emerged as a critical research area in response to the widespread dissemination of manipulated visual content across various online platforms. This paper introduces a novel approach for detect...
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This paper studies the challenging black-box adversarial attack that aims to generate adversarial examples against a black-box model by only using output feedback of the model to input queries. Some previous methods i...
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This paper studies the challenging black-box adversarial attack that aims to generate adversarial examples against a black-box model by only using output feedback of the model to input queries. Some previous methods improve the query efficiency by incorporating the gradient of a surrogate white-box model into query-based attacks due to the adversarial transferability. However, the localized gradient is not informative enough, making these methods still query-intensive. In this paper, we propose a Prior-guided Bayesian Optimization (P-BO) algorithm that leverages the surrogate model as a global function prior in black-box adversarial attacks. As the surrogate model contains rich prior information of the black-box one, PBO models the attack objective with a Gaussian process whose mean function is initialized as the surrogate model's loss. Our theoretical analysis on the regret bound indicates that the performance of P-BO may be affected by a bad prior. Therefore, we further propose an adaptive integration strategy to automatically adjust a coefficient on the function prior by minimizing the regret bound. Extensive experiments on image classifiers and large vision-language models demonstrate the superiority of the proposed algorithm in reducing queries and improving attack success rates compared with the state-of-the-art black-box attacks. Code is available at https://***/yibo- miao/PBO-Attack.
Email is one of the most used modes of communication by many industries and IT sectors. Even common people used to communicate through email about business related information over the internet. As technology grows, t...
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Ransomware attacks have emerged as a significant threat to organizations and individuals, causing substantial financial and operational damages worldwide. With the increasing sophistication and frequency of ransomware...
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The cryptographic techniques that underpin current network security standards run the risk of becoming outdated due to the advancement of quantum computing. Researchers and industry professionals are working to create...
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