# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ2--Techniques and Strategies
#### 4.2.9 Other Techniques  
Other approaches that differ from the previously discussed techniques have been identified in recent edge intelligence studies. Gossip Training, for instance, is a technique where each node updates its hosted DNN model locally during the gradient update step and then shares its information with another randomly selected node in the mixing update step. These steps are repeated until all the DNN converge and reach a consensus. The aim of GoSGD [114] is to address the issue of speeding up the training of convolutional networks. Instead, another gossip-based algorithm--gossiping SGD [115]--is designed to retain the positive features of both synchronous and asynchronous SGD methods. Gossiping SGD replaces the all-reduce joint operation of synchronous training with a gossip aggregation algorithm, achieving asynchrony. [PERSON] et al. [69] present a decentralized stochastic gradient descent method to solve large linear regression problems that can be applied in learning/predicting seismic anomalies via real-time imaging. The decentralized reduce operation of the algorithm is based on Gossip Averaging. The method is then applied to a real problem using an edge computing testbed. Results showed it outperformed many alternative methods. In [PERSON] et al. 's [116] approach, parallel transfer learning is used between edge nodes, where aggregation models allow straightforward parallelization to distribute the computations on individual experts. The authors propose a new factorized training strategy and principled aggregation model, named Tr-BCM, for transfer learning to accelerate full transfer Gaussian processes with large-scale source inputs. Similarly, Hypothesis Transfer Learning (HTL) is used by [PERSON] et al. [21] with a deep learning pipeline of processing stages across the edge, cloudlet, or fog resources. HTL is a standard machine learning technique used to train models on separate disjoint training sets and then transfer the different parts--instead of data--to reach a unique learning model. In addition to the quick and dynamic adapting models using on-device resources, \begin{table}
\begin{tabular}{c c} \hline \hline
**Techniques** & **Works** \\ \hline Federated Learning & [39; 71; 72] \\ Model Partitioning & [34; 35; 38; 68] \\ Model Right-sizing & [34; 68; 82] \\ Edge Pre-Processing & [29; 83; 84] \\ Scheduling & [36; 41; 67; 90] \\ Cloud Pre-Training & [42; 84; 94] \\ Edge Only & [23; 38; 40; 43; 73; 74; 96; 97] \\ Model Compression & [15; 45; 46; 55; 64; 65; 91; 108; 109; 110; 111; 107; 112; 113] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: References considering techniques and/or strategies of edge intelligence implementations. Figure 4: Model compression example. the technique also bolsters the privacy of user-specific information while keeping it on a local device. [PERSON] et al. [117] present an assistant robot based on EI, which incorporates two devices capable of classifying emotional states and physical activities performed by the user. The training uses Keras [118], and after obtaining the H5 model, there is a transformation to TensorFlow lite and, finally, the K model is obtained with a Mobilenet network [119]. [PERSON] et al. [47] use a central fog node as responsible for health data pre-processing with load management and model-ensemble-based prediction with a fully connected NN. In order to achieve load balance, multiple nodes are designed for collaborative analysis. In [PERSON] et al. 's [50] work, a Non-Intrusive Load Monitoring (NILM) algorithm takes real-time measurements, including power, current, and power factor. It identifies the type of appliance considering a dataset for appliance characteristics. There is an offline training phase and an online application phase. In the training phase, the database for appliance characteristics is established using given data of the measurements and the corresponding type of appliances. When the measurement data are acquired, raw data balancing is performed, then feature scaling is conducted.