01973nam 2200361 450 99657466800331620231215134636.01-5044-7053-210.1109/IEEESTD.2021.9382202(CKB)5590000000440557(NjHacI)995590000000440557(EXLCZ)99559000000044055720231215d2021 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrier3652.1-2020 - IEEE guide for architectural framework and application of federated machine learning /IEEE[Place of publication not identified] :IEEE,2021.1 online resourceFederated 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.Computational intelligenceSimulation methodsMachine learningMathematical modelsComputational intelligenceSimulation methods.Machine learningMathematical models.006.3NjHacINjHaclDOCUMENT9965746680033163652.1-2020 - IEEE Guide for Architectural Framework and Application of Federated Machine Learning2584948UNISA