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  2. Federated learning - Wikipedia

    en.wikipedia.org/wiki/Federated_learning

    Federated learning typically applies when individual actors need to train models on larger datasets than their own, but cannot afford to share the data in itself with others (e.g., for legal, strategic or economic reasons). The technology yet requires good connections between local servers and minimum computational power for each node. [3]

  3. Federated Learning of Cohorts - Wikipedia

    en.wikipedia.org/wiki/Federated_Learning_of_Cohorts

    The Federated Learning of Cohorts algorithm analyzes users' online activity within the browser, and generates a "cohort ID" using the SimHash algorithm [13] to group a given user with other users who access similar content.

  4. Federated architecture - Wikipedia

    en.wikipedia.org/wiki/Federated_architecture

    "In the absence of a global authority, the federated architecture has to resolve two conflicting requirements: the components must maintain as much autonomy as possible, but the components must be able to achieve a reasonable degree of information sharing" (Heimbiger, 1985). This is the reason federated architecture strongly demands for governance.

  5. Federated identity - Wikipedia

    en.wikipedia.org/wiki/Federated_identity

    A federated identity in information technology is the means of linking a person's electronic identity and attributes, stored across multiple distinct identity management systems. [1] Federated identity is related to single sign-on (SSO), in which a user's single authentication ticket, or token, is trusted across multiple IT systems or even ...

  6. Multi-agent reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Multi-agent_reinforcement...

    Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. [ 1 ] Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are opposed to the ...

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    mail.aol.com

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  8. Local differential privacy - Wikipedia

    en.wikipedia.org/wiki/Local_differential_privacy

    With federated learning coupled with local differential privacy, researchers have found this model to be quite effective to facilitate crowdsourcing applications and provide protection for users' privacy. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in its storage. Likewise ...

  9. SEC and Big Ten are prepared to push for changes to CFP ...

    www.aol.com/sec-big-10-prepared-push-215606144.html

    While the CFP contract from 2026 through the 2031 season requires the SEC and Big Ten to consult other leagues about prospective changes to the playoff system, it also provides them with the ...