## 2 Materials and Methods
### Federated Learning for Articulated Robot Control
#### 2.3.1 FL Module
However, in the case of the FL-based robot control performance improvement framework proposed in this study, if existing problem-solving methods are applied, the problems that may occur when applying it to an actual manufacturing environment can be minimized. First, in terms of security issues in manufacturing sites, from the perspective of network infrastructure, the OT network to which industrial robots and PLCs are connected can be improved by constructing a closed network separated from the IT network, to which manufacturing execution systems (MESs) and enterprise resource planning (ERP) are connected [48], and from the perspective of hardware, it is possible to resolve the inherent security problems that may occur in a manufacturing environment (security vulnerabilities in the equipment and infrastructure themselves, excluding themalicious actions of workers within the process) by installing security equipment such as security switches and firewall equipment [49]. Next, the unbalanced data problem (difficulty in securing data for specific classes, such as bad classes) can be solved by utilizing the Data Augmentation technique [50] and the few-shot learning technique [51]. In addition, it is expected that communication problems between devices will continue to be solved in the future as the development of high-bandwidth communication becomes possible through the development of next-generation wireless and wired communication technologies such as P-5G (Private-5G) and 6G. ## 5 Conclusions

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, and through a reasoning process, it simplifies the process of deriving the picking tasks and execution plans of articulated robots. In addition, the control algorithm of the 3\(-\)DOF articulated robots is devised and utilized to demonstrate how the proposed FL framework recognizes and analyzes the manufacturing environment and part-picking conditions in real time. Experiments were conducted to compare a standard CNN model with the object detection model designed and proposed in this work for low-specification computing devices. Additionally, experiments were performed on a framework that includes multiple robots (or clients) and a single server. Therefore, the proposed methodologies were analyzed and validated using quantified performance metrics. As a result, the proposed object detection model is suitable for application on low-end computing devices, and the framework demonstrates that the FL model can achieve 80% object recognition accuracy under limited resource conditions. Moreover, there are no statistical differences in terms of picking performance between articulated robots at a significance level of 0.05. Therefore, we can conclude that the proposed framework is appropriately designed to perform identical picking tasks via multiple articulated robots. Furthermore, this study is notable for proposing and conducting experiments on methodologies based on actual hardware to assess the viability of robots in flexible automation systems that manufacturing companies can implement to compete on the market. This approach contrasts with existing federated learning, which has primarily been utilized in the medical and telecommunications sectors due to concerns about data privacy. Furthermore, experiments conducted on low-specification hardware devices to verify the applicability of the technology for SMEs demonstrate the applicability of the methodology proposed in this study for various hardware specifications and manufacturing environments. As a result, this study is expected to advance the research on model architecture for non-GPU low-end specification hardware, which is one of the methodologies applied in the proposed framework, and the research on multiple complex structures of articulated robots, such as real manufacturing environments.