## 5 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset** & **Studies Referenced** \\ \hline \multirow{8}{*}{CNN} & Cifar-10 & [48,53–55,60–62,66,70,73,76,79,84,86,97–103,105,108,111,115,119,122,128] \\  & MNIST & [48,54,59–61,66,67,07,72,75,82,84,86,93,97,98,100,104,105,108,114,119,122,128] \\  & FMNIST & [48,51,53,59–61,73,77,79,81,86,89,97,99,100,105,117,122,126] \\ \cline{2-3}  & FEMNIST & [56,76,77,99,109,113–115] \\ \cline{2-3}  & EMNIST & [72,93,95,104] \\ \cline{2-3}  & Other & [50,62,73,80,81,88,90,93,98,102,112,114,134] \\ \hline \multirow{8}{*}{ResNet} & Cifar-10 & [71,74,78,91,107,118] \\ \cline{2-3}  & Cifar-100 & [52,69,71,76,83,136] \\ \cline{2-3}  & Tiny ImageNet & [55,62] \\ \cline{2-3}  & Other & [52,55,69,71,74,101,118] \\ \hline \multirow{8}{*}{VGG} & Cifar-10 & [69,72,74,75,106,113,114,117,124,130] \\ \cline{2-3}  & Cifar-100 & [72,106,113,114] \\ \cline{2-3}  & SVHN & [69,74,124] \\ \cline{2-3}  & Other & [69,124,136] \\ \hline \multirow{4}{*}{LSTM} & Shakespeare & [50,76,99,111,114,115,125] \\ \cline{2-3}  & Other & [51,95,111,123] \\ \hline \multirow{8}{*}{MLP} & MNIST & [57,69,77,98,102,108] \\ \cline{2-3}  & FEMNIST & [69,77,109] \\ \cline{2-3}  & FMNIST & [77,129] \\ \cline{2-3}  & Other & [77,108,123,129] \\ \hline \multirow{4}{*}{LeNet} & MNIST & [68,101,107,117] \\ \cline{2-3}  & Cifar-10 & [52,92,93,96] \\ \cline{2-3}  & FMNIST & [107,117,130] \\ \cline{2-3}  & Other & [92,96] \\ \hline \multirow{4}{*}{MLR} & Synthetic & [77,84,125] \\ \cline{2-3}  & MNIST & [59,77,84] \\ \cline{2-3}  & FEMNIST & [77,111] \\ \cline{2-3}  & FMNIST & [59] \\ \hline \multirow{4}{*}{MobileNet} & Cifar-10 & [106,107] \\ \cline{2-3}  & Cifar-100 & [106,136] \\ \cline{2-3}  & Tiny ImageNet & [136] \\ \hline \multirow{2}{*}{SVM} & MNIST & [68,129] \\ \cline{2-3}  & Other & [123] \\ \hline \multirow{2}{*}{FCN} & MNIST & [50,62,131] \\ \cline{2-3}  & Other & [131] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The non-IID data challenge studies for the top most commonly used learning models with the respective datasets utilized when employing these models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset** & **Studies Referenced** \\ \hline \multirow{8}{*}{CNN} & MNIST & [146,149,152,154,165,169–171,180,182,185,189,192,202,203,208–210,212] \\ \cline{2-3}  & Cifar-10 & [140,146,167,171,176,184,188–191,204,210] \\ \cline{2-3}  & FMNIST & [143,152,167–169,190,203,212] \\ \cline{2-3}  & FEMNIST & [140,142,167,185] \\ \cline{2-3}  & EMNIST & [150,169,203,210] \\ \cline{2-3}  & Cifar-100 & [190] \\ \hline \multirow{4}{*}{ResNet} & Cifar-10 & [143,144,148,159,161,168,174,179,180,182,184–186,193,200,201,208,212,213] \\ \cline{2-3}  & Cifar-100 & [144,161,199,213] \\ \cline{2-3}  & Other & [141,160,166,200–202,206,208,213] \\ \hline \multirow{4}{*}{Logistic Regression} & MNIST & [157,176,180,190,198] \\ \cline{2-3}  & Cifar-10 & [173,180,192] \\ \cline{2-3}  & FMNIST & [178,207] \\ \cline{2-3}  & Other & [147,157,162,167,178,193] \\ \hline \multirow{4}{*}{LeNet} & MNIST & [148,174,181,183,195,211] \\ \cline{2-3}  & Cifar-10 & [151,159] \\ \cline{2-3}  & FMNIST & [158] \\ \hline LSTM & Other & [142,149,159,165,167,195] \\ \hline \multirow{4}{*}{VGG} & Cifar-10 & [141,144,148,158,161,195] \\ \cline{2-3}  & Other & [141] \\ \hline \multirow{2}{*}{Neural Network} & MNIST & [155,157] \\ \cline{2-3}  & Other & [198,209] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The communication-efficient studies for the top most commonly used learning models with the respective datasets utilized when employing these models.