LEADER 01973nam 2200361 450 001 996574668003316 005 20231215134636.0 010 $a1-5044-7053-2 024 7 $a10.1109/IEEESTD.2021.9382202 035 $a(CKB)5590000000440557 035 $a(NjHacI)995590000000440557 035 $a(EXLCZ)995590000000440557 100 $a20231215d2021 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$a3652.1-2020 - IEEE guide for architectural framework and application of federated machine learning /$fIEEE 210 1$a[Place of publication not identified] :$cIEEE,$d2021. 215 $a1 online resource 330 $aFederated 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. 606 $aComputational intelligence$xSimulation methods 606 $aMachine learning$xMathematical models 615 0$aComputational intelligence$xSimulation methods. 615 0$aMachine learning$xMathematical models. 676 $a006.3 801 0$bNjHacI 801 1$bNjHacl 906 $aDOCUMENT 912 $a996574668003316 996 $a3652.1-2020 - IEEE Guide for Architectural Framework and Application of Federated Machine Learning$92584948 997 $aUNISA