# Rebirth of Distributed AI--A Review of eHealth Research
## 2 Major Contributions of the Paper  
In this paper, we focus on discussing the application and potential impact of federated learning in a major smart city domain, namely e-Health. Major contributions of the paper may be summarized as follows:  
* Look into the eHealth of the future: Discussion on the envisioned eHealth services and applications, their design requirements, and the potential interplay of their enabling technologies. * Analyses of the literature surveys about federated learning and related topics. This will direct the readers to the correct source. * Federated learning overview: A tutorial section to equip readers with the background information that will enable them to comprehend all aspects of federated learning. \begin{table}
\begin{tabular}{l l} \hline \hline  & **Abbr.** & **Full Text** \\ \hline EHR & \multicolumn{2}{c}{Electronic Health Records} \\ \hline ELPC & \multicolumn{2}{c}{extremely low-power communications} \\ \hline eMBB & \multicolumn{2}{c}{Enhanced Mobile Broadband} \\ \hline ERLLC & \multicolumn{2}{c}{extremely reliable and low latency communication} \\ \hline FeMBB & \multicolumn{2}{c}{further-enhanced mobile broadband} \\ \hline FL & \multicolumn{2}{c}{Federated Learning} \\ \hline HE & \multicolumn{2}{c}{Homomorphic Encryption} \\ \hline HIPPA & \multicolumn{2}{c}{Health Insurance Portability and Accountability Act} \\ \hline IoMT & \multicolumn{2}{c}{Internet of Medical Things} \\ \hline IoT & \multicolumn{2}{c}{Internet of Things} \\ \hline ITS & \multicolumn{2}{c}{intelligent transportation system} \\ \hline LDHMC & \multicolumn{2}{c}{long distance and high-mobility communications} \\ \hline LPWA & \multicolumn{2}{c}{Low Power Wide Area} \\ \hline M2M & \multicolumn{2}{c}{Machine-to-Machine} \\ \hline MBBLL & \multicolumn{2}{c}{mobile broad bandwidth and low latency} \\ \hline mBBMT & \multicolumn{2}{c}{massive broad bandwidth machine type} \\ \hline MEC & \multicolumn{2}{c}{Mobile Edge Computing} \\ \hline mLLMT & \multicolumn{2}{c}{massive low latency machine type} \\ \hline QoL & \multicolumn{2}{c}{Quality of Life} \\ \hline QoS & \multicolumn{2}{c}{Quality of Service} \\ \hline SMPC & \multicolumn{2}{c}{Secure Multi-Party Computation} \\ \hline THz & \multicolumn{2}{c}{Terahertz} \\ \hline uHDD & \multicolumn{2}{c}{Ultra-High Data Density} \\ \hline uHSLLC & \multicolumn{2}{c}{Ultra-High-Speed with Low-Latency} \\ \multicolumn{2}{c}{Communications} \\ \hline uMUB & \multicolumn{2}{c}{Ubiquitous Mobile Ultra-Broadband} \\ \hline URLLC & \multicolumn{2}{c}{Ultra-Reliable Low-Latency Communications} \\ \hline VR & \multicolumn{2}{c}{Virtual Reality} \\ \hline WPAN & \multicolumn{2}{c}{Wireless Body Area Network} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Federated learning challenges: We pack the challenges under different categories, which assist in categorizing the solution approaches of the research literature. * Literature survey of the FL solution approaches: An exhaustive survey of the research literature on FL solutions is carried out. To enhance the readability and analyze the impact of solution approaches in addressing the challenges, we map the solution approaches to the categories of challenges. * Federated learning in a major application domain: driven by the expertise and research interests of the authors, we focus on one major application domain in this regard, i.e., eHealth. Figure 1 shows structure of the paper, which depicts positioning of major contributions and relevant literature. There have been a few review surveys on federated learning recently with or without a concentration on a specific industry or domain. For example, [11] gives a detailed survey about federated learning in mobile edge networks. It thoroughly reviews the different implementations and challenges. Specifically, the survey focuses on the challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale.