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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.
Many AI platforms use Wikipedia data, [273] mainly for training machine learning applications. There is research and development of various artificial intelligence applications for Wikipedia such as for identifying outdated sentences, [ 274 ] detecting covert vandalism [ 275 ] or recommending articles and tasks to new editors.
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.
Real-world use cases and integrations [ edit ] Real-world use cases for Deeplearning4j include network intrusion detection and cybersecurity, fraud detection for the financial sector, [ 21 ] [ 22 ] anomaly detection in industries such as manufacturing, recommender systems in e-commerce and advertising, [ 23 ] and image recognition. [ 24 ]
Quantum Machine Learning: Quantum Algorithms and Neural Networks, [27] ISBN 9783111342276, 2024, De Gruyter; Federated Learning for Digital Healthcare Systems, [28] ISBN 9780443138973, 2024, Elsevier; Access Control and Security Monitoring of Multimedia Information Processing and Transmission, [29] ISBN 978-1-83953-693-9, 2023, IET Press
Whenever one of the teams learns a new strategy, the opposing team adapts its strategy to give the best possible counter. When the hiders learn to use boxes to build a shelter, the seekers respond by learning to use a ramp to break into that shelter. The hiders respond by locking the ramps, making them unavailable for the seekers to use.
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A Tsetlin machine is a form of learning automaton collective for learning patterns using propositional logic. Ole-Christoffer Granmo created [1] and gave the method its name after Michael Lvovitch Tsetlin, who invented the Tsetlin automaton [2] and worked on Tsetlin automata collectives and games. [3]