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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.
While research in single-agent reinforcement learning is concerned with finding the algorithm that gets the biggest number of points for one agent, research in multi-agent reinforcement learning evaluates and quantifies social metrics, such as cooperation, [2] reciprocity, [3] equity, [4] social influence, [5] language [6] and discrimination. [7]
Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring a model of the environment . It can handle problems with stochastic transitions and rewards without requiring adaptations.
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
Moreover, numerous graph-related applications are found to be closely related to the heterophily problem, e.g. graph fraud/anomaly detection, graph adversarial attacks and robustness, privacy, federated learning and point cloud segmentation, graph clustering, recommender systems, generative models, link prediction, graph classification and ...
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]
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.
The foundation for RLHF was introduced as an attempt to create a general algorithm for learning from a practical amount of human feedback. [ 6 ] [ 3 ] The algorithm as used today was introduced by OpenAI in a paper on enhancing text continuation or summarization based on human feedback, and it began to gain popularity when the same method was ...