Abstract
New technology is needed to meet the latency and bandwidth issues present in cloud computing architecture specially to support the currency of 5G networks. Accordingly, mobile edge computing (MEC) came into picture as novel emerging solutions to overcome many cloud computing issues. In this contemporary technology, the computation server and processing units are nearby edge servers to reduce latency, increase the network bandwidth and reduce energy consumption in user devices. These features can integrate with several domains such as the internet of things, artificial intelligence (AI), federated learning (FL) and fog computing, etc., to make the system more robust, elastic, efficient, and accurate. Regardless of the advantages, MEC faces several challenges, including security and privacy, deployment protocols, and offloading management. Although, various studies have been found tuning MEC to solve such challenges, the literature provide more ideas for smart developments toward applications particularly using FL and AI. Most researches miss combining interesting aspects of MEC, such as machine learning and deep learning approaches limiting works to only single aspect. Thus, a literature work is needed to focus on all the aspects of MEC together. This study aims to present a comprehensive survey on MEC by providing all necessary information, including network architecture, advantages, objectives, access technologies, deployment templates, characteristics, and many more. The work is not limited to only MEC background but also covers the AI and FL approaches used within MEC, allowing mobile phones to learn a shared predictive model collaboratively. This survey also provides information regarding security and privacy challenges as well as attacks on MEC and their solutions. The applications of MEC illustrate different sectors where MEC is applicable further highlighting open issues and challenges to be investigated.