## 3 Insights into Federated Learning
### FL Domains  
FL has been applied worldwide. In this subsection, some representative works are presented.  
* FL in IoT: [PERSON], et al. [56] was inspired by the dearth of a thorough study on FL's application in IoT. The authors first discuss current developments in FL and IoT to close this gap and offer thoughts on how they may be combined. FL is used in important IoT services, such as IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. They also share the most recent advancements in integrated FL-IoT applications in several important use case areas, such as smart healthcare, smart transportation, UAVs, smart city, and smart industries, which have since caught the public interest. [PERSON], et al. [60] made a survey to provide an outline of smart city sensing and its existing difficulties. They also discussed how FL could help to solve those difficulties. Both the state-of-the-art methods for FL and their use in smart city sensing are covered in detail; clear insights on unresolved problems, difficulties, and possibilities in this area are given as advice for the researchers looking into this topic.
* FL in Mobile: Some discussions on FL in mobile can be found in [43; 57; 58; 59]. [PERSON], et al. [58] provided a tutorial on FL and an in-depth analysis of the problems with FL implementation. The impetus for mobile edge computing is explained at the outset of the study, along with how FL may be used as an enabling technology for group model training at mobile edge networks. The fundamentals of DNN model training, FL, and system architecture for FL at scale are then discussed. The authors then offer thorough assessments, analyses, and comparisons of several implementation strategies for newly developing difficulties in FL. The cost of communication, resource distribution, data privacy, and data security are among the problems. Additionally, they discuss obstacles, future research prospects, and the use of FL for privacy-preserving mobile edge network optimization.
* FL in Healthcare: [PERSON], [PERSON] et al. [45] foresee a federated for future digital health. The authors share their opinion with the community to provide context and detail about the advantages and impacts of FL for medical applications and highlight important considerations and challenges of implementing FL for future digital healthcare. There have been some lasted works in this scope, such as: a cloud-edge Network [66], a survey [67], and a survey in privacy preservation [68].
* Securing FL: [PERSON], [PERSON], et al. [61] examine FL's current weaknesses and then conduct a literature assessment of potential attack strategies aimed at FL's privacy protection features. A fundamental taxonomy is then used to characterize these assault strategies. They also offer a literature review of the most recent FL defense techniques and algorithms designed to counter these assaults. By the relevant fundamental defense concept, these defensive tactics are grouped. The use of a single defensive approach, according to the authors, is insufficient to offer sufficient defense against all known assault techniques.