# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ2--Techniques and Strategies
#### 4.2.4 Edge Pre-Processing  
Another mechanism often used to reduce the size of the data in transit through the network is to manipulate it in the edge tier. This method is commonly used in multimedia applications. In [PERSON] et al. 's [2018] paper, the data captured by end nodes/devices suffer some preliminary processes at the edge and after processed, smaller in size, they are sent to the server (at the cloud). A framework is proposed to classify and reduce environmental noises in conversations through smartphones. Smartphones or recorders send audio to the edge cloudlet via RAN (Radio Access Network) and the it performs the initial processing. Then these data are sent to the core of the network. Cloudlet is a small-scale data center or cloud located at the border of the internet. Its objective is to bring cloud-computing capabilities closer to the consumer. They are typically used for mobile consumers or devices. CNN is adapted and integrated for the Mel-spectrogram. The latter is a good representation of short-time varying sounds, while CNN is good for other types of sounds. Both algorithms integration make big mobile data, integrating an urban environment robust classification system applied to any sound classification. [PERSON] [2018] presents an example of an audio-visual application. In the example, the edge carries out the speech and video processing such as extraction of pitch and feature extraction. In [PERSON] [2018], the author use images from data entry, and they are pre-processed and segmented in the border before being sent to the server where they will pass through a DNN. A similar approach of this mechanism can be found in [PERSON] [2020], where the edge is responsible for feature extraction and algorithm selection. The algorithm selected is run in the cloud. Compressive Sensing (CS) [2018] was proposed to sample and compress the signals simultaneously with the sampling rate far lower than the Nyquist sampling rate based on the sparsity. Meanwhile, accurate reconstruction is achievable. Without losing the information, the signal can be sampled with the fewest observations to reduce its dimensions and save the cost of sampling and transmission. CS can be regarded as a cryptosystem when the random measurement matrix is used as a key [2018]. However, the measurement matrix used as a key is inconvenient for communication since it has a huge size far larger than the size of plain text; therefore, Chaotic Compressive Sensing (CCS) was proposed to solve the problem of key communication [2018]. In other words, a large-size measurement matrix is replaced by a few values. A formal analysis of results of the employment of edge pre-processing shows how small changes in the prediction accuracy can enable substantial performance improvements. Between the factors that affect the computational cost, there is the number of features ex tracted from the data. [PERSON] et al. [48], for example, claim that computational effort can be reduced by 80% assuming a decline of the classification accuracy of only 3%. This work utilizes data pre-processing, a three-point median filter to smooth the signal followed by a segmentation process and a feature selection method (Chi2 filtering [88]), which consists of a discriminating process to find essential features that have more weight in the model. It reduces the dimension of the feature matrix by removing the irrelevant features. For the original sampling provided by the dataset, using only the top three features penalized the results by 3.19%. However, from the baseline parameters (all features and all the signal components) to the final simplified stage (three features and only the most representative component), the time reduction was 81.24% for a laptop, 82.05% for a 540 Raspberry Pi and 92.32% for a Raspberry Zero. Feature selection powered by swarm search is used as a pre-processing method for improving the accuracy and speed of local Fog data analytics.