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  2. Federated learning - Wikipedia

    en.wikipedia.org/wiki/Federated_learning

    Diagram of a Federated Learning protocol with smartphones training a global AI model. Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients) collaboratively train a model while keeping their data decentralized, [1] rather than centrally stored.

  3. Graph neural network - Wikipedia

    en.wikipedia.org/wiki/Graph_neural_network

    Moreover, numerous graph-related applications are found to be closely related to the heterophily problem, e.g. graph fraud/anomaly detection, graph adversarial attacks and robustness, privacy, federated learning and point cloud segmentation, graph clustering, recommender systems, generative models, link prediction, graph classification and ...

  4. Local differential privacy - Wikipedia

    en.wikipedia.org/wiki/Local_differential_privacy

    Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in its storage. Likewise, differential privacy (DP) attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated learning.

  5. Federated architecture - Wikipedia

    en.wikipedia.org/wiki/Federated_architecture

    Federated Enterprise Architecture is a collective set of organizational architectures (as defined by the enterprise scope), operating collaboratively within the concept of federalism, in which governance is divided between a central authority and constituent units balancing organizational autonomy with enterprise needs.

  6. Federated Learning of Cohorts - Wikipedia

    en.wikipedia.org/wiki/Federated_Learning_of_Cohorts

    Federated Learning of Cohorts (FLoC) is a type of web tracking. It groups people into "cohorts" based on their browsing history for the purpose of interest-based ...

  7. Symbolic artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Symbolic_artificial...

    New deep learning approaches based on Transformer models have now eclipsed these earlier symbolic AI approaches and attained state-of-the-art performance in natural language processing. However, Transformer models are opaque and do not yet produce human-interpretable semantic representations for sentences and documents.

  8. Data envelopment analysis - Wikipedia

    en.wikipedia.org/wiki/Data_envelopment_analysis

    Data envelopment analysis (DEA) is a nonparametric method in operations research and economics for the estimation of production frontiers. [1] DEA has been applied in a large range of fields including international banking, economic sustainability, police department operations, and logistical applications [2] [3] [4] Additionally, DEA has been used to assess the performance of natural language ...

  9. Glossary of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Glossary_of_artificial...

    Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...