# Federated Learning and Its Role in the Privacy Preservation of IoT Devices
## 2 Related Works
### FL Developments  
The advancement of FL is not well-known in culture as a modern privacy-preserving paradigm. The following examples show how federated learning works. We suppose that many different companies collaborate to learn standards. Additionally, it is almost impossible to collect the data of all parties without the consent of the users [28; 57]. Alternatively, a company should use its data to train an ML model framework. It assumes that all groups create a working model, but due to their companies' minimal and incomplete data, it is hard to train a suitable ML [58; 59]. The goal of FL is to find solutions to these problems. The FL ensures that no details about their business location are revealed. Boundaries are shared between clients and server encryption to create a global model based on the non-violation of privacy [60].  
Since its inception in 2016, FL has constantly been evolving [61; 62; 63; 64]. This section also considers the following open policies (asynchrony, security, and privacy) in addition to the fundamentals (as described above). In the event of a dangerous attack on dispersed devices, FL can lead to data leakage while it helps protect sensitive data [29; 65; 66]. For example, such a leak could be caused by stochastic gradient descent (SGD) in the application process. This makes it challenging to protect privacy and safety in Florida. For algorithm performance, IoT infrastructure's rapid growth in network traffic has become a major technical concern. FL's ability to link a variety of devices needs the use of efficient algorithms to identify running applications [67; 68]. For example, the Federated Averaging (FedAvg) algorithm is used for local computation and update computation and privacy separation algorithms to minimize time overhead. The FL-related algorithms still need to be optimized when dealing with big data due to limited computing power. For the application of technology [69; 70; 71; 72], FL greatly influences smart city applications. It covers almost every aspect, especially finance, medical care, transportation, etc. With FL, models can be trained on data corresponding to different levels [73; 74]. FL will train models that cannot be federated directly by hospitals, such as smart healthcare.  
On the other hand, FL uses input-sensitive data without compromising privacy or conquering the data key. The correctness of a model could be significantly enhanced by combining big data [75; 76]. IoT devices will become more intelligent through the successful use of FL. Table 3 shows the year-wise contribution to the research of FL.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Ref** & **Contribution** \\ \hline
2016 & [1] & Introduce the term FL \\
2016 & [77] & To enhance the functioning of the global model and \\  & & decrease communications load. \\
2017 & [48; 78] & Studies of attacks on privacy. \\
2018 & [67; 72; 76; 79; 80] & Development of resource allocation strategies \\
2019 & [5; 71; 81] & Proof of FL in Blockchain \\
2019 & [14; 37] & Improving privacy using FL \\
2019 & [25; 44] & Resource allocation strategies \\
2019 & [39; 43; 50; 57] & Applied Federated Learning in wireless communications \\  & & on mobile edge \\
2019 & [47; 49; 51] & Applied Federated Learning on-device personalization \\
2019 & [59; 62; 82] & Applied Federated Learning for data privacy in big data \\ \hline \hline \end{tabular}
\end{table}
Table 3: Year-wise contribution to the research of federated learning.  
The gradual expansion of FL has opened new opportunities for people from all aspects of life. This paper addresses the use of FL in smart cities, including communications, healthcare, and the Internet of Things [85; 89; 90]. Smart cities are expected to grow presently due to the use of FL. A more naturalistic environment that enriches everyone will be created by FL participating in all aspects of life.