## 2 Background
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Paper** & **Cited \#** & **Year** & **Area** & **Contribution** \\ \hline
[25] & 1200 & 2020 & FL, Mobile Edge Network & FL enables collaborative training and DL for mobile edge network optimization \\
[26] & 423 & 2021 & FL and IoT & Survey of FL applications in IoT networks \\
[27] & 320 & 2021 & FL and ML & Comparing different ML-based deployment architectures on FL \\
[28] & 313 & 2020 & FL and ML & Overview of FL enabling technologies, protocols, and applications \\
[29] & 310 & 2021 & FL and IoT & Recent advances of federated learning towards enabling federated learning-powered IoT applications \\
[30] & 268 & 2023 & FL and data mining & Comprehensive review on federated learning systems \\
[31] & 267 & 2021 & FL and ML & Survey desirable criteria and future directions in communication and networking systems \\
[32] & 240 & 2023 & FL and edge computing & Explores the domain of personalized FL and taxonomy of PFL techniques \\
[33] & 237 & 2022 & FL and IoT & Surveys problem statements and emerging challenges of applying FL within heterogeneous IoT \\
[34] & 194 & 2020 & FL and IoT & Surveys existing studies on FL and its use in wireless IoT \\
[35] & 151 & 2020 & FL and ML & Highlights the need for personalization and surveys recent research on this topic \\
[36] & 145 & 2022 & FL and ML & Discusses several approaches that address the performance issues associated with FL impact on the security and overall performance of the IoT \\
[37] & 134 & 2020 & FL & Data-driven learning model-based cooperative localization and location data processing with emerging machine learning and big data methods \\
[38] & 119 & 2022 & FL and cybersecurity & Extensive study on the ability of FL to provide better cybersecurity and prevent various cyberattacks in real times \\
[21] & 88 & 2022 & FL & Surveys the incentive mechanism design for federated learning \\
[39] & 87 & 2021 & FL and cybersecurity & Comprehensive survey of the unique security vulnerabilities exposed by the FL ecosystem \\
[40] & 76 & 2022 & FL and cybersecurity & Comprehensive survey on privacy and robustness in FL over the past five years \\
[41] & 66 & 2022 & FL and deep learning & Analyzes and presents the main ideas based on differential privacy (DP) to guarantee users’ privacy in DL and FL \\
[42] & 64 & 2022 & FL and blockchain & Survey on the synergy of FL and blockchain to enable drone edge intelligence for green sustainable environments \\
[43] & 51 & 2023 & FL and IoT and cybersecurity & Survey on the existing intrusion detection solutions proposed for the IoT ecosystem including IoT devices, fog computing, and cloud computing layers \\
[44] & 49 & 2021 & FL and ML & Reviews existing contemporary works and Explains the challenges of each type of FL survey \\
[45] & 46 & 2023 & FL and IoMT & Presents privacy-related issues in IoMT \\
[46] & 35 & 2022 & FL and blockchain & Presents a solution taxonomy of BC-based FL in UAVs for B5G networks \\
[47] & 33 & 2023 & FL and IoT & Provides a novel federal classification between cloud, edge, and fog, and presents a comprehensive research roadmap on offloading for different federated scenarios \\
[48] & 25 & 2022 & FL and data privacy & Survey reviews the Privacy-Preserving Aggregation (PPAgg) protocols \\
[49] & 23 & 2022 & FL and privacy & Discusses the current state of research on blockchain and FL \\
[50] & 21 & 2020 & FL & Reviews related studies of FL to base on the baseline a universal definition gives a guiding for the future work \\
[51] & 21 & 2022 & FL and blockchain & Proposes an FL-based layered healthcare informatics architecture along with the case study on FL-based electronic health records (FL-EHR) \\
[52] & 20 & 2024 & FL and adversarial attacks & Provides a comprehensive understanding of the attacks’ effect by identifying FL attacks with low budgets, low visibility, and high impact. \\ \hline \hline \end{tabular}
\end{table}