# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
High latency [8]: offloading intelligence tasks to the edge enables achievement of faster inference, decreasing the inherent delay in data transmission through the network backbone; Security and privacy issues [9; 10]: it is possible to train and infer on sensitive data fully at the edge, preventing their risky propagation throughout the network, where they are susceptible to attacks. Moreover, edge intelligence can derive non-sensitive information that could then be submitted to the cloud without further processing; The need for continuous internet connection: in locations where connectivity is poor or intermittent, the ML/DL could still be carried out; Bandwidth degradation: edge computing can perform part of processing tasks on raw data and transmit the produced data to the cloud (filtered/aggregated/pre-processed), thus saving network bandwidth. Transmitting large amounts of data to the cloud burdens the network and impacts the overall Quality of Service (QoS) [11]; Power waste [12]: unnecessary raw data being transmitted through the internet demands power, decreasing energy efficiency on a large scale. The steps for data processing in ML vary according to the specific technique in use, but generally occur in a well-defined life cycle, which can be represented by a workflow. Model building is at the heart of any ML technique, but the complete life cycle of a learning process involves a series of steps, from data acquisition and preparation to model deployment into a production environment. When adopting the Edge intelligence paradigm, it is necessary to carefully analyze which steps in the ML life cycle can be successfully executed at the edge of the network. Typical steps that have been investigated for execution at the edge are data collection, pre-processing, training and inference. Considering the aforementioned steps in ML and the specific features of edge nodes, we can identify many challenges to be addressed in the edge intelligence paradigm, such as (i) running ML/DL on devices with limited resources, (ii) ensuring energy efficiency without compromising the inference accuracy; (iii) communication efficiency; (iv) ensuring data privacy and security in all steps; (v) handling failure in edge devices; and (vi) dealing with heterogeneity and low quality of data. In this paper, we present the results of a systematic literature review on current state-of-the-art techniques and strategies developed for distributed machine learning in edge computing. We applied a methodological process to compile a series of papers and discuss how they propose to deal with one or more of the aforementioned challenges. The purpose of this survey is to present the outcome of a recent literature review on EI, identify key components, and analyze the retrieved studies thoroughly, correlating techniques, strategies, frameworks and application domains. To achieve this end, we first present the challenges we found on EI; secondly, we discuss all techniques and strategies we found related to pre-processing federated learning and scheduling; thirdly, we describe the frameworks we found for supporting EI, as well as which group of techniques and strategies they are mainly related to; and fourthly, the paper presents a taxonomy of application domain areas, which involves industry, surveillance, security, intelligent transport systems, and health and energy management. We also present a comparison of our work with related works (surveys and others) we found in the literature, as well as presenting open issues and future directions. The rest of this paper is organized as follows: Section 2 describes related surveys we found in literature, as well as a comparison with this one. Section 3 approaches our research methodology to support the reproducibility of this systematic review. Section 4 presents our findings aligned to our Research Questions (ROs), Section 3.