# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ3--Frameworks for Edge Intelligence  
This section describes the studies that provided answers to the RQ3 of this survey. Table 8 lists the main frameworks currently used in distributed ML applications. The table also correlates each framework with the corresponding EI group of techniques or the main related strategy. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Framework**} & **Groups of** & \\  & **Techniques or** & **Comments** \\  & **Strategies** & \\ \hline Neurosurgeon [126] & Model Partitioning & Lightweight scheduler to automatically partition DNN computation between edge devices and cloud at the granularity of NN layers \\ \hline JointDNN [127] & Model Partitioning & JointDNN provides an energy- and performance-efficient method of querying some layers on the mobile device and some layers on the cloud server. \\ \hline [PERSON]. [PERSON] et al. [35] & Model Partitioning & They divide the NN layers and deploy the part with the lower ones (closer to the input) into edge servers and the part with higher layers (closer to the output) into the cloud for offloading processing. They also propose an offline and an online algorithm that schedules tasks in Edge servers. \\ \hline Musical chair [128] & Model Partitioning & Musical Chair aims at alleviating the compute cost and overcoming the resource barrier by distributing their computation: data parallelism and model parallelism. \\ \hline AAIoT [129] & Model Partitioning & Accurate segmenting NNs under multi-layer IoT architectures \\ \hline MobileNet [46] & Model Compression & Presented by Google Inc., the two hyperparameters introduced allow the model builder to choose the right sized model for the specific application. \\ \hline Squeezenet & Model & Compression & It is a reduced DNN that achieves AlexNet-level accuracy with 50 times fewer parameters \\ \hline \hline \end{tabular}
\end{table} 
Table 8: EI frameworks. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Framework**} & \multicolumn{2}{c}{**Groups of Techniques or**} & \multirow{2}{*}{**Comments**} \\  & **Strategies** & \\ \hline Tiny-YOLO & Model & Compression \\  & & \\  & & \\  & & \\  & & \\  & & \\ \hline BranchyNet & Right sizing & Open source DNN training framework \\  & & that supports the early-exit mechanism. \\ \hline TeamNet [130] & Model Compression & TeamNet trains shallower models using the similar but downsized architecture of a given SOTA (state of the art) deep model. The master node compares its uncertainty with the worker’s and selects the one with the least uncertainty as to the final result. \\ \hline OpenEI [46] & Model Compression & \\  & Data Quantization & The algorithms are optimized by compressing the size of the model, quantizing the weight. The model selector will choose the most suitable model based on the developer’s requirement (the default is accuracy) and the current computing resource. \\ \hline TensorFlow & Data Quantization & TensorFlow’s lightweight solution, which is designed for mobile and edge devices. It leverages many optimization techniques, including quantized kernels, to reduce the latency. \\ \hline QNNPACK (Quantized Neural Networks PACKage) & Data Quantization & Developed by Facebook, is a mobile-optimized library for high-performance NN inference. It provides an implementation of common NN operators on quantized 8-bit tensors. \\ \hline ProtoNN [133] & Model & Compression \\  & & Inspired by k-Nearest Neighbor (KNN) and could be deployed on the edges with limited storage and computational power. \\ \hline EMI-RNN [134] & Right Sizing & It requires 72 times less computation than standard Long Short term Memory Networks (LSTM) and improves its accuracy by 1\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._