Improving load forecasting is becoming increasingly crucial for power system management and operational research. Disruptive influences can seriously impact both the supply and demand sides of power. This work examine...
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Federated learning is a distributed learning solution that achieves high-quality machine learning models while ensuring privacy and collaboration among various end devices. However, different kinds of end devices can ...
Federated learning is a distributed learning solution that achieves high-quality machine learning models while ensuring privacy and collaboration among various end devices. However, different kinds of end devices can lead to an unstable training process due to limited and dynamic communication and computation resources. Federated Dropout is an important technique for mitigating such resource bottlenecks by randomly removing a fixed percentage of activations of model components. However, most of the existing FL dropout methods cannot effectively utilize the distinguished characteristics of different clients or fail to automatically adapt to end device heterogeneity. In this paper, we thus propose Adaptive Federated Dropout with Reinforcement Learning (AFD-RL). AFD-RL employs reinforcement learning technology to adapt to the clients’ unbalanced computation and communication resources and provide a personalized dropout strategy for each client. By automatically selecting suitable model components for each client in each communication round, AFD-RL achieves better accuracy with fewer communication rounds. Extensive experiments validate the effectiveness of the proposed approach in improving training efficiency and inference accuracy.
Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge Computing (MEC) extends computing resource...
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Terrestrial Radio Propagation (TRP) involves radio wave propagation from one station to another over the surface of the earth. Radio communication systems have been deployed for broadcasting, mobile cellular, and publ...
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Building an effective fashion recommendation system is challenging due to the high level of subjectivity and the semantic complexity of the features involved. Users’ decision depends largely on their interest and the...
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ISBN:
(纸本)9781450397810
Building an effective fashion recommendation system is challenging due to the high level of subjectivity and the semantic complexity of the features involved. Users’ decision depends largely on their interest and the appearance of the product. Such information is often hidden in implicit feedback from users' purchase histories and product images. Most interest-based recommendation systems like Deep Interest Network (DIN) and Deep Interest Evolution Network (DIEN) only take advantage of product attributes and context review, which are all text information. There are also some studies focusing on the use of image features for a fashion product recommendation. They try to extract features from images and recommend products based on their similarity. However, for DIEN. It works not well when there are few interactions between users and items, the model can not find users' interests effectively. On the other hand, these image-based recommendation systems tend to ignore an important factor: the user's interest. We propose a new system trying to find users' interests by introducing the visual information of the product and our image-based deep interest attention model based on DIEN. Product attributes and user preference can both be represented by introducing visual information. We can model similar attribute items in the same place and find user interests more effectively. We conducted a series of experiments on our own dataset to compare user click-through rate (CTR) AUC and Accuracy using our model with DIEN and other existing *** experimental results demonstrate that visual information effectively aids CTR prediction and achieved better recommendation results.
Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy. Data users have been endowed with the 'right to be forgotten' of their data. In ...
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With the development of communication technologies,various mobile devices and different types of mobile services became *** emergence of these services has brought great convenience to our *** multi-server architectur...
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With the development of communication technologies,various mobile devices and different types of mobile services became *** emergence of these services has brought great convenience to our *** multi-server architecture authentication protocols for mobile cloud computing were proposed to ensure the security and availability between mobile devices and mobile ***,most of the protocols did not consider the case of hierarchical *** the existing protocol,when a mobile user once registered at the registration center,he/she can successfully authenticate with all mobile service providers that are registered at the registration center,but real application scenarios are not like *** some specific scenarios,some mobile service providers want to provide service only for particular *** this reason,we propose a new hierarchical multi-server authentication protocol for mobile cloud *** proposed protocol ensures only particular types of users can successfully authenticate with certain types of mobile service *** proposed protocol reduces computing and communication costs by up to 42.6%and 54.2%compared to two superior *** proposed protocol can also resist the attacks known so far.
Integrating low-light image enhancement techniques, in which diffusion-based AI-generated content (AIGC) models are promising, is necessary to enhance nighttime teleoperation. Remarkably, the AIGC model is computation...
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Federated learning, an emerging distributed learning paradigm, offers significant advantages and holds promise for addressing trust issues, breaking down data silos, and enabling active data sharing in the realm of co...
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Federated learning, an emerging distributed learning paradigm, offers significant advantages and holds promise for addressing trust issues, breaking down data silos, and enabling active data sharing in the realm of consumer electronics. However, traditional federated learning faces critical security and privacy challenges in this domain, including single-point attacks, inference attacks, and poisoning attacks. To address these pressing security concerns, this paper proposes a decentralized federated learning framework focusing on security, reliability, and efficiency, tailored to meet the sustainability demands of consumer electronics. The proposed framework is founded upon masking and secret sharing techniques, establishing an emphatic privacy-preserving federated learning framework that ensures the security of gradient data and robustness against participant dropouts. Additionally, we actively motivate high-quality participants to collaborate by incorporating an incentive mechanism. Building upon the enhancement of existing federated learning approaches reliant on masking techniques, the method outlined in this paper significantly reduces communication overhead while preserving accuracy. Empirical research results comprehensively substantiate the superiority of this approach. Furthermore, compared to prevalent blockchain-based federated learning methods, our approach makes noteworthy strides in accuracy and efficiency. IEEE
Memristor stateful logic based on a two-dimensional (2D) memristor crossbar array (MCBA) has been proved to be an effective approach to break the von Neumann bottleneck. But the three-dimensional (3D) array which has ...
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