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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. A defining characteristic of federated learning is data heterogeneity.
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 ...
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.
Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases, [26] ISBN 9780443296543, 2024, Elsevier Quantum Machine Learning: Quantum Algorithms and Neural Networks, [27] ISBN 9783111342276, 2024, De Gruyter
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.
[27] [28] A model server serves the parametric machine-learning models that makes decisions about data. It is used for the inference stage of a machine-learning workflow, after data pipelines and model training. A model server is the tool that allows data science research to be deployed in a real-world production environment.
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Johannes Caspar, the Data Protection Commissioner of Hamburg, Germany, told Wired UK that FLoC "leads to several questions concerning the legal requirements of the GDPR," explaining that FLoC "could be seen as an act of processing personal data" which requires "freely given consent and clear and transparent information about these operations."