## 2 Background
### Model Management
This methodology enables rapid model aggregation, maintaining the pertinence and precision of forecasts in dynamic settings. By employing strategies such as the integration of real-time noise injection to ensure privacy and the implementation of continuous monitoring, the online deployment of the model ensures its ability to promptly adapt to developing trends. Feedback loops facilitate the collection of user interactions in real time, enabling prompt modifications and refinements. Utilizing adaptive learning rates and personalization settings guarantees customized experiences for individual users. Online deployment generally ensures that FL models offer timely, accurate, customized predictions while protecting user privacy. This makes it crucial for applications that require swift and exact answers to real-time data streams. Conversely, offline deployment in FLSs encompasses using pretrained models on novel data without necessitating real-time adaptation. After the FL model completes the training and aggregation process by incorporating updates from devices involved in the process, it can be implemented offline for many applications. Offline deployment is especially advantageous in situations when immediate adjustment is not critical and regular updates are satisfactory. In this particular situation, the model that has undergone training is implemented on servers or edge devices, enabling it to provide predictions or services by leveraging its accumulated knowledge. This deployment strategy demonstrates efficacy when employed in applications characterized by consistent data patterns and when privacy-preserving methodologies have been included during the training phase. Although offline deployment may not possess the immediate responsiveness of online deployment, it offers the advantage of ensuring consistency and accuracy in predictions. This characteristic renders offline deployment well-suited for numerous FL applications. Table 2 provides a comparison of online and offline model deployment in FLSs. The selection of online or offline deployment in FLSs is contingent upon the particular use case, data patterns, privacy stipulations, and the necessity for real-time adaption. Each option presents distinct advantages, enabling organizations and developers to customize their strategy according to the application's specific requirements. \begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Adaptability**} & Online deployment is well-suited for dynamic environments subject to rapid change, as it enables instant adaptation to new data. In contrast, offline deployment ensures consistency but may not adapt as rapidly to new circumstances. \\ \hline \multirow{2}{*}{**Privacy**} & Both deployment strategies prioritize privacy during the training period. However, online deployment guarantees real-time privacy maintenance while updating the model, offering enhanced privacy for continuous interactions. \\ \hline \multirow{2}{*}{**Resource usage**} & Online deployment requires consistent and instantaneous information exchange, as well as the availability of computational resources to implement model revisions promptly. In contrast, offline deployment reduces the need for continuous communication, enhancing resource efficiency. \\ \hline \multirow{2}{*}{**Use cases**} & Online deployment is highly advantageous in scenarios where real-time adjustments and customized responses are crucial. Offline deployment is a suitable option for applications requiring periodic model updates and consistent forecasts, particularly in situations where continuous communication may not be practical or essential. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between online and offline model deployment in Federated Learning systems. Privacy and security are of utmost importance in the taxonomy of FLSs. The preservation of data privacy, secrecy, and security is critical due to the involvement of various sources.