IEEE 3652.1-2020

Detail information

Designation: IEEE 3652.1-2020

Publication date: 19/03/2021

Pages: 69

Country: International technical standard

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Anotation

New IEEE Standard - Active.
Federated machine learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across repositories owned by different organizations or devices. A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide. It defines the architectural framework and application guidelines for federated machine learning, including description and definition of federated machine learning; the categories federated machine learning and the application scenarios to which each category applies; performance evaluation of federated machine learning; and associated regulatory requirements.

ISBN: 978-1-5044-6892-3, 978-1-5044-7053-7, 978-1-5044-7054-4
Number of Pages: 69
Product Code: STDAPE24312, STD24407, STDPD24407
Keywords: computation efficiency, economic viability, federated machine learning (FML), IEEE 3652.1™, incentive mechanism, machine learning, model performance, privacy, privacy regulations, security
Category: Computer Communications and Networking|Robotics
Draft Number: P3652.1/D6.1, Jul 2020 - APPROVED DRAFT