# Federated Learning for Edge Computing: A Survey
## 5 Applications
### Summary
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Year** & **Application Type** & **Description** \\ \hline
[103] & 2019 & Mobile devices & Google GBoard for Android phones for predicting and forecasting user input \\ \hline
[104] & 2019 & Mobile devices & Keyboard to predict emoji based on text entered by the user \\ \hline
[105] & 2020 & Mobile devices & Apple uses FL to train a model for speech recognition \\ \hline
[107] & 2020 & Mobile devices & A privacy-preserving mobility prediction framework based on phone data via FL \\ \hline
[108] & 2019 & Mobile devices & Intelligent use of cooperation among mobile devices and edge nodes to exchange parameters for optimization of calculations, caching, and communication \\ \hline
[110] & 2019 & IoT systems & Autonomous self-learning distributed anomaly detection system for IoT devices \\ \hline
[111] & 2020 & IoT systems & It focuses on predicting user behavior in smart homes and provides a simple ML model with a time structure to achieve a decent trade-off between accuracy, communication, and computational cost \\ \hline
[130] & 2021 & IoT systems & Survey and overview provided new insights into applications in IoT, development tools, communication efficiency, security and privacy, migration, and scheduling in edge FL \\ \hline
[45] & 2021 & IoT systems & A set of metrics such as sparsity, robustness, quantification, scalability, security, and privacy are defined to rigorously evaluate FL, proposing a taxonomy for FL in IoT networks \\ \hline
[131] & 2022 & IoT systems & Anomaly detection on edge devices that can detect disruptions in IoT networks \\ \hline
[132] & 2019 & IoT systems & Design and use of the FL framework for joint allocation of communication and computational resources \\ \hline
[112] & 2020 & Industry & The platform, which was deployed in a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based security monitoring solutions in smart city applications \\ \hline
[113] & 2019 & Industry & Utilize FL in an industrial setting for tasks related to visual inspection of products; this approach could address manufacturing processes’ weaknesses while offering manufacturers privacy guarantees \\ \hline
[114] & 2018 & Industry & Analysis and processing of large amounts of data and its subsequent use in the automotive, energy, robotics, agriculture, and healthcare industries \\ \hline
[13] & 2022 & Industry & Deployment possibilities of FL and its digital twin in different deployment scenarios, such as smart cities \\ \hline
[115] & 2020 & Industry & FL based on blockchain to implement asynchronous collaborative machine learning among distributed clients that own the data, with real historical data available from network systems \\ \hline
[116] & 2022 & Industry & Use of FL in the manufacture of components for aerospace companies \\ \hline
[117] & 2019 & Healthcare & Differential private learning for electronic health records via FL \\ \hline
[118] & 2021 & Healthcare & Demonstration and adaptation of FL techniques for healthcare and electronic systems in healthcare such as drug discovery and disease prediction systems \\ \hline
[119] & 2019 & Healthcare & FL community-based algorithm to predict mortality and length of hospital stay; electronic health records are clustered into communities within each hospital based on standard medical characteristics \\ \hline
[120] & 2018 & Healthcare & Forecast hospitalizations for patients with heart disease during a target year \\ \hline
[121] & 2019 & Healthcare & Implemented a distributed system to address intrusion detection in medical cyber-physical systems \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of federated learning applications.