## 4 Discussion  
This paper provides an introduction to FL while retaining focus on the communication component of FL. Communication is quite an essential part of the federated learning environment. It is essentially how the global machine learning model is transmitted from the central server to all of the participating devices. Similarly, the model is then trained on the local data that are available on the devices and then uploaded back to the central server. This constant communication requires many downloads and uploads using a reliable network bandwidth. As this is not always the case, a limited bandwidth or poor client participation can create a communication lag in completing the FL training. Techniques used to make the rounds of communication more efficient are shared in this paper. For example, a wide range of compression scheme methods mentioned in this paper can help to reduce the communication overheads and reduce the costs to some factor. Even though the methods presented in this paper make the communication front more efficient, they are all implemented using various resources, such as different computational power and datasets. There are dependable ways for communication rounds to be addressed. However, due to the range of solutions presented, it could be concluded that a culmination of techniques implemented together could result in the most communication-robust solution for an FL setting.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Section** & **Model and Technology** & **Remarks** \\ \hline
[61] & FedPAQ & Using quantization techniques based upon model accuracy \\ \hline
[89] & Compression Schemes-Quantization & Lossy FL algorithm (LFL) & Quantizing models before broadcasting \\ \hline
[92] & Compression Schemes-Quantization & Hyper-sphere quantization (HSQ) framework & Ability to reduce the cost of communication per iteration \\ \hline
[92] & UVeQFed & Algorithm convergence of model minimizes the loss function \\ \hline
[94] & & Heir-Local-QSGD & Leveraging client–edge–cloud network hierarchy and quantized models updates \\ \hline
[81] & Decentralized Training or Peer-to-peer Learning & BrainTorrent & A peer-to-peer learning framework where models converge faster and reach good accuracy \\ \hline
[78] & Learning & QuanTimed-DSGD & decentralized gradient-based optimization imposing iteration deadlines for devices \\ \hline
[66] & FedMCCS & A multi-criteria client selection that considers IoT device specification and network condition \\ \hline
[67] & & Resource allocation model & Optimizing learning performance in how clients are selected and how bandwidth is allocated \\ \hline
[68] & Client Selection & The framework allows the server to aggregate as many clients as possible within a certain time-frame \\ \hline
[70] & & Power-of-choice & A communication and computation-efficient client selection framework \\ \hline
[72] & & A decentralized deep learning model & Ability to handle different phases of the model training well \\ \hline
[73] & & A partitioned variational inference (PVI) & A Bayesian neural network over FL that is synchronous and asynchronous for model updates across machines \\ \hline
[75] & Updates & One-shot federated learning & A single round of communication performed between central server and connected devices \\ \hline
[76] & & FOLB & Intelligent sampling of devices in each round of model training to optimize the convergence speed \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cont.