## 2 Background
### Communication
Asynchronous communication, for instance, makes it easier for research institutes located in several time zones to collaborate, enabling scientists to share their discoveries without being constrained by synchronized communication periods. To fully utilize asynchronous communication's advantages in FLSs, its associated problems must be addressed. The selection of one of these two approaches is contingent upon the particular demands and limitations of the FLS and the attributes of the involved devices or nodes. Table 5 provides a summary of the comparison between asynchronous and synchronous communication in the context of FLSs. * Communication overhead: Within the context of FL, the term \"communication overhead\" refers to the additional data transmission and processing resources necessary for participants to exchange model updates, gradients, and other information while the collaborative learning process is being carried out [39]. The effective management of communication overhead is essential because it directly affects the bandwidth of the network, the latency, and the overall effectiveness of the FLS. In FLSs, a number of different techniques have been established to reduce the amount of communication overhead. Table 6 presents a comprehensive summary of several prominent methodologies. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Synchronous Communication** & **Asynchronous Communication** \\ \hline \multirow{4}{*}{Communication Timing} & Participants communicate according to a predefined schedule or specific synchronization points. & Participants communicate independently, sending updates whenever they have new data or model improvements to contribute. \\ \hline \multirow{4}{*}{Flexibility} & Less flexible as participants are bound to fixed communication schedules, potentially causing delays for some participants. & Highly flexible, allowing participants to operate at their own pace, accommodating varying network conditions and device availability. \\ \hline \multirow{4}{*}{Dependency on central control} & Often requires central control to coordinate communication, ensuring all participants adhere to the predefined schedule. & Reduces dependency on central control, enabling decentralized decision-making and autonomous operation of participants. \\ \hline \multirow{4}{*}{Latency} & Lower latency as updates are synchronized, allowing rapid model adjustments and real-time responses to changing data patterns. & Potentially higher latency due to the lack of synchronization, especially if updates from critical participants are delayed. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between asynchronous and synchronous communication in FLSs. Each of the strategies mentioned above are designed to target distinct facets of communication overhead in the context of FL. Frequently, these methodologies are synthesized in practical contexts to attain maximum communication efficacy while concurrently unfolding the principles of data confidentiality, model precision, and system promptness. The selection of methodologies is contingent upon the particular application scenario, prevailing network circumstances, and attributes of the involved devices. On the other hand, the presence of significant communication overhead in FLSs can be attributed to several variables. These variables include the utilization of large model sizes, frequent updates, non-selective participant communication, high data dimensionality, non-localized computing, and excessive reliance on encryption or privacy measures. The transmission of machine learning models across distant devices can result in massive data transmission and consume significant network resources, particularly when these models are sizable or updated often. The practice of non-selective communication further exacerbates the issue because all participants send updates without considering their relevance.