# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ1--Research Challenges in Edge Intelligence (EI)  
In this section, we summarize the challenges faced by the Edge Intelligence (EI) paradigm that the analyzed studies either mentioned or aimed to tackle. The discussion presented in this section aims to provide answers to RQ1: What are the main challenges and open issues in the distributed learning field? As mentioned earlier, performing ML techniques at the edge of the network promises to bring several benefits, but it raises several challenges. As this field is still in its beginning, solutions to such challenges are still being investigated. The surveyed studies tackle several challenges, which can be broadly grouped into six categories, displayed in Table 5 and described in what follows. CH1 consists of dealing with the typical low processing power of edge devices. Edge devices often have little processing capacity, mainly when compared to the powerful data centers at the cloud. On the other hand, many ML applications require high computational power that outweighs the possibilities of resource-constrained IoT and edge devices. Limited resources also include memory and storage capacities. NN and ML algorithms generally require storing of and access to a handful of parameters that describe the model architecture and weight values forming the classification model. With limited storage, it may not be possible to have continued access to the original training data, or the data may have been removed altogether to free up space. Therefore, a significant challenge is reducing memory access and storing the data locally to avoid costly reading and writing to external memory modules. CH2 consists of ensuring the energy efficiency of edge devices without compromising the accuracy of the system. In general, the higher the complexity of the required processing, the more energy is consumed. Edge devices can be battery-powered. In these cases, the energy consumption of algorithms must be minimized to reach energy efficiency. However, \begin{table}
\begin{tabular}{c l} \hline \hline \multicolumn{2}{c}{**Challenges**} \\ \hline CH1 & Running ML/DL on devices with limited resources \\ CH2 & Ensuring energy efficiency without compromising the accuracy \\ CH3 & Communication efficiency \\ CH4 & Ensuring data privacy and security \\ CH5 & Handling failure in edge devices \\ CH6 & Heterogeneity and low quality of data \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Challenges in distributed machine learning in edge computing. this should be done with care so as not to compromise the quality of the data generated and the decisions/inferences made. So, there is an important trade-off to be managed. CH3 concerns communication issues, where edge intelligence models must consider that the devices might face poor connectivity. In such cases, the model update time in training tasks may be delayed. [PERSON], [PERSON] and [PERSON] [21] claim that the inference is highly sensitive to the available bandwidth in communication. Challenges in communication include network traffic, fluctuations in the bandwidth, intermittent or unavailable connectivity. CH4 is related to data privacy and security. Several applications in edge intelligence handle sensitive data, such as healthcare. Thus, distributed ML algorithms must be able to preserve user privacy and information security when data are transferred throughout the devices. Distributed Edge-Intelligence (EI) has multiple points of vulnerability to possible malicious attacks or leakage of confidential or important data in the ML workflow. CH5 is the challenge posed by failures in edge devices. Since devices might fail at some point, the distributed algorithm must consider ways to overcome this situation. Lastly, heterogeneity and lack of quality in available data rise challenge CH6. For most ML algorithms, especially in supervised machine learning, high accuracy depends on the high quality of training data. However, this often does not apply in edge intelligence scenarios, where the collected data are sparse and unlabelled [10].