## 2 Background
### Communication
In addition, many gradients must be transmitted for high-dimensional data, resulting in an additional increase in communication volume. When computations are concentrated in a central location, participants must send unprocessed data, resulting in inefficiencies. Furthermore, an excessive focus on encryption and privacy protocols can increase the quantity of data, thus intensifying the difficulties associated with communication. The presence of inefficient communication protocols can exacerbate these concerns. To address the issue of high communication overhead, techniques such as model compression, intelligent participant selection, dimensionality reduction, localized computation, and the judicious application of encryption methods must be strategically implemented. This ensures a balance between the protection of data and the effectiveness of communication. Federated Learning Algorithms  
FL algorithms are yet another essential component of FLSs. These algorithms make it possible to train collaborative models without transferring raw data between devices and a central server. This helps protect users' privacy while reducing the amount of communication overhead required. The following is a list of essential uses of FL algorithms. \begin{table}
\begin{tabular}{c c c} \hline  & **Synchronous Communication** & **Asynchronous Communication** \\ \hline Communication overhead & More predictable communication patterns, potentially reducing overall communication overhead. & Can lead to higher communication overhead due to the lack of synchronization, efficient data compression and differential updates are essential to manage this. \\ \hline Adaptability to dynamic environments & Might struggle to adapt to dynamic environments where network conditions or participant availability fluctuate. & More adaptable to dynamic environments, allowing participants to contribute whenever they can, ensuring continuous collaboration. \\ \hline Fault tolerance & Susceptible to disruptions if a participant fails to communicate at a scheduled time, potentially affecting the entire synchronization process. & More fault-tolerant as one participant’s failure does not disrupt the entire system. Other participants can continue to contribute independently. \\ \hline Privacy and security & Easier to implement security protocols and encryption as communication occurs at predictable times. & Requires robust encryption and security measures to ensure the safety of data transmitted independently by participants. \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._* Optimization algorithms: Optimization algorithms have a crucial function in FLSs, providing the aggregation of information from various devices and enabling the construction of accurate and efficient machine learning models. These algorithms have been specifically developed to achieve a harmonious equilibrium between the collaborative aspects of FL and the imperative requirements of privacy preservation and computational efficiency [21]. Federated optimization techniques commonly prioritize minimizing a global objective function by integrating local updates obtained from individual devices. Several examples of prominent optimization algorithms employed in FLSs are mentioned below:
* Federated SGD: This is a pivotal algorithm that has revolutionized the framework of collaborative machine learning in decentralized environments, particularly in the context of Federated Learning Systems (FLSs). This algorithm offers a