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Related Concept Videos

Surveys02:16

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Surveys are essential for marking property boundaries near water bodies. Different types of surveys are defined, each with its own function. Land surveys mark the property boundaries, while route surveys determine the position of properties on nearby highways. Topographic surveys create maps by capturing the three-dimensional features of the land. Hydrographic surveys focus on the shapes of underwater areas and the movement of streams through the properties. Mine surveys determine the relative...
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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.
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A Survey on Federated Recommendation Systems.

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    Federated recommendation systems (FedRSs) enhance user privacy by training models with intermediate parameters, not raw data. This survey reviews FedRS challenges like privacy, security, and efficiency, guiding future research.

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    Area of Science:

    • Artificial Intelligence
    • Computer Science
    • Information Security

    Background:

    • Federated learning (FL) is increasingly used in recommendation systems to safeguard user privacy.
    • FL enables model training using aggregated parameters, bypassing direct access to sensitive user data.
    • Federated Recommendation Systems (FedRSs) offer collaborative improvements while adhering to privacy regulations.

    Purpose of the Study:

    • To provide a comprehensive survey of the current research landscape in Federated Recommendation Systems (FedRSs).
    • To identify and analyze key challenges including privacy, security, heterogeneity, and communication costs.
    • To guide researchers and practitioners by summarizing progress and highlighting future research directions.

    Main Methods:

    • Literature review and synthesis of existing research on FedRSs.
    • Categorization and analysis of privacy mechanisms, security attacks and defenses.
    • Summarization of approaches for addressing system heterogeneity and communication efficiency.

    Main Results:

    • Detailed overview of common privacy-preserving techniques in FedRSs, with their respective pros and cons.
    • Review of emerging security threats and corresponding defense strategies.
    • Compilation of methods to mitigate heterogeneity and reduce communication overhead.
    • Introduction to practical FedRS applications and benchmark datasets.

    Conclusions:

    • FedRSs present a promising approach for privacy-preserving recommendations but face significant technical hurdles.
    • Addressing privacy, security, heterogeneity, and communication efficiency is crucial for widespread adoption.
    • This survey identifies critical research gaps and outlines promising avenues for future advancements in FedRSs.