## 1 Introduction
Since FL was introduced, there has been a growing interest in the research communities to explore the opportunities and capabilities of FL in different domains. A range of industries are researching various examples of the FL capability to maximize the benefits of this decentralized approach, including transportation [12; 13], autonomous vehicles [14], and internet of things (IoT) applications [15; 16; 17; 18]. Gboard by Google for predictive texting is another simple and reliable implementation of FL, where a federated averaging algorithm is utilized to train the ML model over the on-device decentralized data to improve predictive texting results while also reducing the privacy and security risks of the sensitive user data compared to centralized ML approaches [19; 20; 21; 22]. However, with the introduction of any new technology, the more benefits reaped from its capabilities, the more challenges that are exposed, and FL is not an exception. There has been a significant amount of challenges regarding the security and privacy of  
Figure 1: How FL can be used to improve predictive healthcare ML using sensitive data across multiple hospitals [23]. FL. When it comes to security, there are three main concepts that should be considered in designing an FL environment, including confidentiality, integrity, and availability. Authors in [24] demonstrated the current vulnerabilities to the FL environment as communication protocols, data manipulation, central server, aggregation strategy, and implementation team. Due to such vulnerabilities, FL could be faced with various types of threats, including poisoning, inference, backdoor attacks, etc. Some of the these threats are shared with traditional ML models, whereas some of them are dedicated to FL settings. There have been many efforts to enhance the security and privacy preservation across different settings for FL, including secure multi-party computation [25], differential privacy [26], adversarial training [27], etc., and they are still in progress. The readers are encouraged to visit the referred surveys to obtain a greater understanding of the security and privacy aspects of FL. As FL takes place in a distributed environment, end-devices are required to be connected over a reliable network to constantly share their updates. Factors such as end-device network connections operate at substantially lower rates when compared to network connections that are available at a data center. This form of unreliability in FL communication could be potentially expensive in the training process [28]. Therefore, some research has been conducted towards making the communication factor of an FL environment more efficient. For example, quantization and sparsification are two model compression techniques that can be integrated with the FL averaging algorithms to make communication more effective. It is an interesting area of research and there have been various efforts to make FL communication more reliable, but very limited surveys are found on this topic. Therefore, this survey paper aims to present a summary of recent research conducted on the subject. In this paper, the current challenges, proposed models, and possible methods for enhancing the FL models' efficiency with respect to communication are presented and discussed. The remainder of this paper is structured as follows. Section 2 provides an introduction to the FL system and how it functions. Section 3 introduces and answers the research questions (RQs). Sections 4 and 5 review the key findings in this paper, where it navigates towards sharing future expectations, concentrating on this topic of research.