# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ3--Frameworks for Edge Intelligence
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Framework**} & \multicolumn{2}{c}{**Groups of**} \\  & **Techniques or** & **Comments** \\  & **Strategies** & \\ \hline FederatedAveraging (FedAvg) [77] & Federated Learning & Combines local stochastic gradient descent (SGD) on each client with a server that performs model averaging. \\ \hline SSGD [76] & Federated Learning & System that enables multiple parties to jointly learn an accurate neural network model for a given objective without sharing their input datasets. \\ \hline BlockFL [138] & Blockchain & Mobile devices’ local model updates are exchanged and verified by leveraging blockchain. \\ \hline Edgent [6] & Model Partitioning & Adaptively partitions DNN computation between the device and edge, in order to leverage hybrid computation resources in proximity for real-time DNN inference. DNN right-sizing accelerates DNN inference through the early exit at a proper intermediate DNN layer to further reduce the computation latency. \\ \hline PipeDream [139] & Model Partitioning & PipeDream keeps all available GPUs productive by systematically partitioning DNN layers among them to balance work and minimize communication. \\ \hline GoSGD [114] & Gossip Averaging & Method to share information between different threads based on gossip algorithms and showing good consensus convergence properties. \\ \hline Gossiping SGD [140] & Gossip Averaging & Asynchronous method that replaces the all-reduce collective operation of synchronous training with a gossip aggregation algorithm. \\ \hline GossipGraD [141] & Gossip Averaging & Asynchronous communication of gradients for further reducing the communication cost. \\ \hline INCEPTIONN [142] & Data Quantization & Lossy-compression algorithm for floating-point gradients. The framework reduces the communication time by 70.9 80.7\% and offers 2.2 3.1\(\times\) speedup over the conventional training system while achieving the same level of accuracy. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._When correlating the EI strategies with frameworks, it is possible to notice some interesting associations. There are ten of these techniques and strategies, of which only three are present in more than 60% of the papers. They are: (i) Model Compression with 24%, (ii) Model Partitioning with 20%, (iii) Data Quantization with 17%. Federated Learning, Right-Sizing, Gossip Averaging and Model Selector correspond to 9% each. The others have less than 8%. Figure 5 illustrates these ten classes of strategies. Among these strategies, Model Compression is the most suitable for solving the process of training and testing with the raw data and reducing the dimensionality in real-time. This strategy allows ML algorithms to have faster responses, using lower resources of bandwidth, power and processing. In addition, this technique has proven to be more economical and better at data security once the processing is realized entirely on the edge. In terms of algorithms, the most common is the DNN paradigm of machine learning, which segments models into successive parts (layers). This algorithm allows for the deployment of each part on distinguished sites (model partitioning). DNN also enables compression techniques such as removing nodes or layers, allowing offloading of a whole model in resource-constrained devices.