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  2. Federated learning - Wikipedia

    en.wikipedia.org/wiki/Federated_learning

    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. A defining characteristic of federated learning is data heterogeneity.

  3. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices.

  4. Data mining - Wikipedia

    en.wikipedia.org/wiki/Data_mining

    The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...

  5. Symbolic artificial intelligence - Wikipedia

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

    Machine learning is not confined to association rule mining, c.f. the body of work on symbolic ML and relational learning (the differences to deep learning being the choice of representation, localist logical rather than distributed, and the non-use of gradient-based learning algorithms).

  6. 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 ...

  7. Liang Zhao - Wikipedia

    en.wikipedia.org/wiki/Liang_Zhao

    Liang Zhao is a computer scientist and academic from China.He is an associate professor in the Department of Computer Science at Emory University. [1]Zhao's research focuses on data mining, machine learning, and artificial intelligence, with particular interests in deep learning on graphs, societal event prediction, interpretable machine learning, multi-modal machine learning, generative AI ...

  8. Distributed artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Distributed_artificial...

    The objectives of Distributed Artificial Intelligence are to solve the reasoning, planning, learning and perception problems of artificial intelligence, especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objective, DAI requires:

  9. Computational intelligence - Wikipedia

    en.wikipedia.org/wiki/Computational_intelligence

    Nowadays, deep learning has become the core method for artificial intelligence. In fact, some of the most successful AI systems are based on CI. [5] However, as CI is an emerging and developing field there is no final definition of CI, [6] [7] [8] especially in terms of the list of concepts and paradigms that belong to it. [3] [9] [10]