## 5 The Use of AI for the Creation of SRE
### Federated Learning
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **Problem Description** & **Algorithm Used** & **Achievement** \\ \hline [PERSON] et al., 2023 [PERSON] et al., 2023 [PERSON] et al., 2023 [50] & Review paper & FL, FL with Deep Deterministic Policy Gradient (FL-DPFG) & Application of ML techniques in RIS-enhanced systems (focus on IoT) \\ \hline [PERSON] et al., 2022 [PERSON] et al., 2022 [51] & Review paper & ML techniques & Application of ML techniques in RIS-enhanced systems (focus on 6G) \\ \hline [PERSON] et al., 2024 [PERSON] et al., 2024 [52] & Review paper & OTA-FL & Application of ML techniques in RIS-enhanced systems (focus on OTA-FL performance and data privacy) \\ \hline [PERSON] et al., 2024 [PERSON] et al., 2024 [53] & Review paper & FL & Application of ML techniques in RIS-enhanced systems (focus on optimization approaches) \\ \hline [PERSON] et al., 2020 [54] & Privacy Preservation & Optimal Beam Reflection (OBR) based on FL & Minimization of centralized data dependencies to ensure robust user privacy \\ \hline [PERSON] et al., 2020 [55] & Preservation of the privacy of user data in a RIS aided mmWave system & FL & Local models are trained, encrypted, and sent to the central server while ensuring private data remains localized on devices \\ \hline [PERSON] et al., 2022 [56] & Reduction in the fading channels effect & FL & Lower training loss and higher test accuracy \\ \hline [PERSON] et al., 2022 [57] & Poor spectrum efficiency in RIS-aided networks & FSL & Better spectrum prediction accuracy and enhanced system utility \\ \hline [PERSON] et al., 2022 [58] & Great transmission overhead that occurs in RIS-aided communication systems for the collection of Channel Status Information & FDReLNet & Significant reduction in transmission overhead \\ \hline \end{tabular}
\end{table} 
Table 5: Selection of research works demonstrating FL. Figure 3: A general representation of FL, as implemented in wireless networks. The surveys listed in Table 5 provide a broad perspective on the application of ML techniques in RIS-enhanced systems, with a particular focus on IoT, FL and optimization approaches [50, 51, 52, 53]. In [50], a detailed examination of ML-based RIS-enhanced Internet of Things (IoT) systems is presented and the potential of RIS to enhance IoT network performance is highlighted. A review of ML techniques for intelligent reflecting surface (IRS)-based wireless communication towards 6G is offered by [PERSON] et al. [51], emphasizing their role in channel estimation, beamforming optimization, and resource allocation. However, one primary challenge identified in both in [50, 51] is the complexity of channel estimation in RIS-enhanced systems, particularly in dynamic environments. Although ML techniques show promise in addressing this issue, they often require substantial training data and computational resources, which may not always be available in practical scenarios. The over-the-air- (OTA-) FL is discussed in [52], and a comprehensive review of its current status, challenges, and future directions is provided. The potential of OTA-FL in preserving data privacy while enabling collaborative learning across distributed devices is highlighted, whereas the dependence of its performance on wireless channel conditions and device heterogeneity is examined, since these factors may cause model convergence issues. The optimization approaches surveyed in [53] are categorized into model-based, heuristic, and ML methods and are analyzed in the context of various RIS applications, such as coverage extension, interference management, and energy efficiency enhancement. The other studies summarized in Table 5 provide various insights into the integration of FL within RIS-enabled wireless communication frameworks. The applicability of FL to heterogeneous and out-of-distribution (OoD) environments has been examined and up to 15% better performance has been achieved by leveraging Nash equilibrium strategies [45].