## 3 Research Questions and Communication Efficient Methods
### RQ1-What Are Some of the Challenges Presented in FL with Regards to Communication? As mentioned in Section 2 regarding the general working of the FL environment, the central server shares the light version of the global model across the network to all of the participating devices. The total number of devices participating in this FL environment can sometimes be in the millions, and the bandwidth over which the devices are connected to the environment can be relatively slow or unstable [49]. In an FL training environment, there can be many rounds of communication between the central server and all of the participating devices. Over a singular communication round, the local model is shared across all of the devices in the FL environment. Each participating device downloads the local model to train it on their local dataset. A version of that is uploaded back to the environment to the central server. Therefore, there is a constant downlink and uplink during the communication rounds [50]. However, due to the limited bandwidth, energy, and power on the device end, these rounds of communication can be slow [48]. Even with these challenges, the overall communication cost of sharing model updates is relatively lower than sharing copious amounts of data from the devices to a central server; it is still important to preserve the communication bandwidth further to make it more efficient [24]. This subsection introduces the overheads [51] or challenges that could create a communication bottleneck in an FL environment. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Reference Year** & **Objective** & \multicolumn{2}{c}{**Security Privacy**} & \multicolumn{2}{c}{**Communication**} & \multicolumn{1}{c}{**Challenges**} & \multicolumn{1}{c}{**Future**} \\ \hline
[29] & 2019 & Providing a survey of existing works on FL, discussing frameworks, concepts, and applications. & ✓ & ✓ & ✗ & ✓ & ✓ \\ \hline
[46] & & Introduction of concept of FL, predominantly covering threat model attacks. & ✓ & ✓ & ✗ & ✓ & ✓ \\
[8] & & An FL survey focusing on hardware, software and technologies, & ✓ & ✓ & ✓ & ✓ & ✓ \\  & & and real-life applications & ✓ & ✓ & ✓ & ✓ & ✓ \\
[30] & 2020 & Introduction of FL, presenting some existing challenges and their solutions & ✓ & ✓ & ✓ & ✓ & ✓ \\
[24] & & A survey on security and privacy of federated learning & ✓ & ✓ & ✗ & ✓ & ✓ \\
[48] & & A detailed survey introducing FL and the challenges. & ✓ & ✓ & ✓ & ✓ & ✓ \\
[41] & & An overview of FL characteristics and applications within three specific domains & ✓ & ✓ & ✓ & ✓ \\ \hline
[39] & & A survey of FL in healthcare, covering common topics of introduction of technology, challenges, etc. & ✓ & ✓ & ✓ & ✓ \\
[24] & & A comprehensive survey posing research questions with regard to FL and privacy and security & ✓ & ✓ & ✓ & ✓ \\
[44] & 2021 & A thorough categorization of FL based on six main aspects to enable effective deployment of FL models & ✓ & ✓ & ✓ & ✓ \\
[45] & & A systematic literature review of FL research studies with a concentration on the medical domain & ✓ & ✓ & ✓ & ✓ \\
[47] & & A comprehensive review of three main aspects of FL: design, applications, and challenges & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Publication analysis of survey papers over the past couple of years. * _Number of participating devices:_ Having a high number of participating devices in an FL environment has its advantage, wherein the ML model could be trained on more data and there could be a possible increase in performance and accuracy. However, the large number of devices participating in multiple FL training rounds at the same time could create a communication bottleneck. In some cases, a high number of clients could also increase the overall computational cost [24; 52]. * _Network bandwidth:_ In contrast to the traditional ML approach, the FL approach reduces the cost substantially; however, the communication bandwidth still needs to be preserved [24]. The participating devices may not always have the bandwidth needed.