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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]
Typical use-cases involve things such as cross-domain, web-based single sign-on, cross-domain user account provisioning, cross-domain entitlement management and cross-domain user attribute exchange. Use of identity federation standards can reduce cost by eliminating the need to scale one-off or proprietary solutions.
Collaborative learning is a situation in which two or more people learn or attempt to learn something together. [1] Unlike individual learning, people engaged in collaborative learning capitalize on one another's resources and skills (asking one another for information, evaluating one another's ideas, monitoring one another's work, etc.).
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.
In industrial search engines, such as LinkedIn, federated search is used to personalize vertical preference for ambiguous queries. [2] For instance, when a user issues a query like "machine learning" on LinkedIn, he or she could mean to search for people with machine learning skill, jobs requiring machine learning skill or content about the topic.
In January 2014, Learning Tools Interoperability version 2.0 was launched, introducing REST-based two-way communication between external tools and the learning platform. [5] Simultaneously, a subset of version 2.0 was released as version 1.2, as a transitional update from version 1.1 to version 2.0.
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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.