Most of the images on the Internet are color images, and steganalysis of color images is a very critical issue in the field of steganalysis. The current proposed color image steganalysis features mainly rely on manual...
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Increasing technological advancements in developing assistants have gained attention because of its wide use in a variety of fields. In the realm of softwareengineering, developers are including these solutions in di...
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The traditional way of using pen and paper to take notes is getting over by the touch screen devices. These devices provide more options to the users to enhance their productivity while taking notes. The ability to re...
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Cloud computing contains large-scale tasks and resources. Currently, the local search is a considerable choice in ensuring both computational complexity and optimization. Based on our previous research on multi-route ...
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Federated learning is widely used in medical applications for training global models without needing local data access. However, varying computational capabilities and network architectures (system heterogeneity), acr...
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Federated learning is widely used in medical applications for training global models without needing local data access. However, varying computational capabilities and network architectures (system heterogeneity), across clients pose significant challenges in effectively aggregating information from non-independently and identically distributed (non-IID) data. Current federated learning methods using knowledge distillation require public datasets, raising privacy and data collection issues. Additionally, these datasets require additional local computing and storage resources, which is a burden for medical institutions with limited hardware conditions. In this paper, we introduce a novel federated learning paradigm, named Model Heterogeneous personalized Federated Learning via Injection and Distillation (MH-pFLID). Our framework leverages a lightweight messenger model that carries concentrated information to collect the information from each client. We also develop a set of receiver and transmitter modules to receive and send information from the messenger model, so that the information could be injected and distilled with efficiency. Our framework eliminates the need for public datasets and efficiently share information among clients. Our experiments on various medical tasks including image classification, image segmentation, and time-series classification, show MH-pFLID outperforms state-of-the-art methods in all these areas and has good generalizability. Copyright 2024 by the author(s)
Managing and exchanging sensitive information securely is a paramount concern for the scientific and cybersecurity community. The increasing reliance on computing workflows and digital data transactions requires ensur...
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User stories are a common notation for expressing requirements, especially in agile development projects. While user stories provide a detailed account of the functional requirements, they fail to deliver a holistic v...
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Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classif...
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Gamification is the incorporation of game elements into non-game settings. software designers increase user motivation by introducing adequately engaging elements, such as leaderboards and badges, into an existing sys...
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Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network's prediction mechanism must be interpretable to establish t...
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