## 2 Materials and Methods
### Federated Learning for Articulated Robot Control
#### 2.3.1 FL Module
This implies that the appropriate setting of hyperparameters can influence the model training performance of FL. Figure 11: Training effectiveness as a function of the percentage of participating clients per round (\(C=\{0.25,\,0.5,\,0.75,\,1.0\}\)), local epochs (\(E=\{5,\,10\}\)), and mini-batch size (\(B=\{5,\,10\}\)): (**a**) training loss versus communication rounds with \(C=0.25\); (**b**) training loss versus communication rounds with \(C=0.5\); (**c**) training loss versus communication rounds with \(C=0.75\); (**d**) training loss versus communication rounds with \(C=1.0\). ## 4 Discussion

This study presented an FL-based framework to achieve operational efficiency in SME manufacturing environments using industrial articulated robots through FL techniques in a low-end equipment environment. The devised framework conducts FL in a non-GPU environment through a reasoning process. However, since the proposed framework is an a priori study on the application of federated learning, which has not yet been widely used in industrial environments, there are some points to note when applying it to existing manufacturing sites. First, as proposed in this study, existing manufacturing equipment can be improved to perform FL. To do this, the location of the camera sensor (gripper or robot support, etc.) must be clearly defined, and the robot controller must be able to transmit the machining parameters (coordinate and angle information of the robot, etc.) as digital signals so that the gripper of the robot can move to the inferred location of a recognized object. In this study, the robot was assembled directly, a camera was installed, and the robot controller was developed so that the robot could be controlled as desired. Second, in the proposed framework, all data currently being worked on are digitized, and since individual robots automatically make decisions about the current work's content, the security of the data within a smart factory is important. This part was not mentioned in this study, but if research on industrial Operation Technology (OT) security, which is becoming more important with the current development of smart factories, is conducted together, it is expected that potential information security issues can be resolved. However, since the proposed framework is designed for network use within the factory (i.e., intra-network use), this will not be a major problem if external access to the factory is properly supervised. The third point is the potential for problems with communication latency, which commonly occurs in the implementation of smart factories and digital twin technologies. This is a problem that can also occur in FL that performs individual client model learning and global model learning, but as described in Section 3.4, if the machine learning time is reduced by selecting appropriate parameters or if model learning and updates are performed during times when the factory has low workloads, the possibility of communication latency in the network can be reduced. In addition, if an independent network is implemented within the factory for FL only, without using the existing network, it will be possible to solve both security and communication latency issues. However, additional research seems necessary for economic feasibility analysis related to improved factory productivity resulting from the establishment of an FL environment. In relation to the aforementioned issues, when applying the proposed framework to an actual manufacturing environment, problems that occur when implementing a smart factory using technologies such as digital twins and AI may occur. Specifically, there are problems such as communication problems between the pieces of data collection equipment (robots, machine vision cameras, etc.) and equipment connected to each piece of data collection equipment and responsible for model learning, unbalanced data problems (in a typical manufacturing environment, it is difficult to obtain data in a defective state), and security problems in OT networks.