## 2 Materials and Methods
### Federated Learning
This iterative process is then repeated until global and local models are converged. However, considering the communication cost of updating the global model every round from selected local clients, FedAVG is a more efficient algorithm than FedSGD, which performs only one local update for each client. In FedAVG, the local training process for the local model of each client is conducted for epochs (\(E\)) on the \(B\) size mini-batched local dataset, and the global model is then updated [25]. In the process of training a model through the FL methodology, the efficiency of the model aggregation is expressed by a quantified metric, namely the convergence rate, which is the number of communication rounds (number of global updates) required to reach the global optimum of the global objective function \(F\). According to [26], the convergence rate of the local SGD, i.e., FedSGD in the previously described FL algorithm, has the following impact on hyperparameters \(K\) and \(B\) in the case of independent and identically distributed (IID) data: \(B\) has an \(\mathcal{O}\Big{(}\sqrt{T}\Big{)}\) relationship with the convergence rate, while \(K\) is positively correlated with the convergence rate. \(T\), as mentioned in the definition of convergence rate with big-O notation, represents the total number of rounds required for the global model to converge (i.e., the total number of applications of an algorithm). However, in heterogeneous data (i.e., in the case of non-IID data), the convergence rate becomes \(\mathcal{O}(1/T)\)[15]. Due to its advantages, FL has been widely incorporated in various industries and research fields. [PERSON] et al. [27] proposed a privacy-preserving block chain-based FL framework for IoT devices that can predict customers' requirements and consumption patterns. Moreover, the FL model has been used in conjunction with smart devices to predict human trajectory and behavior. [PERSON] et al. [28] presented a Privacy-Preserving Mobility (PMF) Framework that can predict human mobility patterns while maintaining a balance between predictive accuracy and privacy protection. [PERSON] et al. [29] presented a machine learning framework for the preservation of patient privacy and medical information via the FL environment, while [PERSON] et al. [30] proposed an FL method to improve classification accuracy based on Electroencephalogram (EEG) data collected from different medical institutes. Considering previous studies, FL is a machine learning methodology applicable to various industries, and given its characteristic of operating without sharing internal client information with a central server, it is also expected to be applicable to the manufacturing industry. However, existing studies have primarily focused on data analysis and prediction itself, without considering the actual computational devices and hardware specifications of the applied targets. Additionally, despite the recent research on and applications of FL, there are still issues that need to be addressed in the field. Firstly, due to the characteristics of FL, which creates a global model through communication with each device, there are limitations in the field of ensuring communication in terms of latency, bandwidth usage, and the synchronization of updates from multiple devices. Moreover, it is necessary to improve the methodology to efficiently conduct the FL process based on a networked system with heterogeneous data, hardware, and software specifications [15].