# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ2--Techniques and Strategies
#### 4.2.7 Edge Only  
In the most extreme cases of distributed learning, the computations are all performed in edge devices or nodes, with the cloud server, when used, acting only as host for data storage. [PERSON] et al. [40] and [PERSON] et al. [43] apply this concept to the context of smart parking. Similar methods are proposed by [PERSON] et al. [96] and [PERSON] [23]. This method tries to bring the computations as close as possible to where the data are collected, reducing the maximum time spent on communications. Privacy and security are also advantages of this method since sensitive and private information will not circulate through the public network.  
OpenEI [46] is a framework with a lightweight deep learning Package Manager similar to TensorFlow Lite, optimized to run AI algorithms at the edge, which guarantees low power consumption and low memory footprint. Its Model Selector algorithm looks for the most suitable one for a specific edge platform based on users' requirements and the capabilities of the hardware platform using the four-element tuple ALEM: \"Accuracy, Latency, Energy, Memory footprint\". In addition, it supports training the model locally.  
Other works, such as those presented by [PERSON] et al. [73] and [PERSON] et al. [97], adopt the blockchain infrastructure in their works. This method works like FL, however without a cloud server to distribute the model. Instead, the MEC nodes serve as data storage and model sharing. The models are trained based on distributed deep learning (DDL) [98], meaning that they are trained independently in each node. When they converge, the parameters are chosen by smart contracts; based on an agreement, consensus between two or more parties can be achieved. Blockchain can be seen as a tamper-resistant distributed system to share and store data among a large number of nodes while maintaining the security and privacy of the network [99].  
[PERSON] et al. [62] introduce a novel algorithm architecture approach to enable real-time low-power CNN processing on edge devices. The core of the proposed approach is utilizing 1D dimensional convolution with an architecture that can truly benefit from the algorithm optimization. The proposed architecture operates near the sensor and avoids storing the streaming data in the main memory. In fact, they avoid unnecessary buffering and create a more latency-friendly and ultimately real-time dataflow architecture.  
[PERSON], [PERSON], and [PERSON] [100] present different use cases for Edge-AI (Artificial Intelligence in the Edge), such as eye-gaze systems and biometric authentication. A key aspect in this study is that the data generated by these applications require a great level of privacy and security response speed. Therefore, the whole acquisition and processing should occur on the device or on the periphery.  
[PERSON] et al. [101] propose a lightweight architecture based on Docker [102] to distribute, deploy and manage cloud and edge applications into the clusters, leveraging EI by means of the Regularized Extreme Learning Machine (RELM) algorithm [103], and experimental results suggest a fast training and testing time compared to traditional ELM and Support Vector Machine (SVM) as presented by the [PERSON] [104] dataset, also having reported slightly better accuracy.