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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.
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.
These existing approaches constitute the basis for the enterprise interoperability framework. Existing interoperability frameworks do not explicitly address barriers to interoperability, which is a basic assumption of this research; they are not aimed at structuring interoperability knowledge with respect to their ability to remove various barriers
Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel , thus able to exploit large scale computation and spatial distribution of computing resources .
Double Q-learning [23] is an off-policy reinforcement learning algorithm, where a different policy is used for value evaluation than what is used to select the next action. In practice, two separate value functions Q A {\displaystyle Q^{A}} and Q B {\displaystyle Q^{B}} are trained in a mutually symmetric fashion using separate experiences.
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
A federated database system (FDBS) is a type of meta-database management system (DBMS), which transparently maps multiple autonomous database systems into a single federated database. The constituent databases are interconnected via a computer network and may be geographically decentralized.
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.