## 1 Introduction  
Smart factories are advanced manufacturing facilities that are highly automated, digitalized, and integrated with state-of-the-art technologies, such as Cyber-Physical Systems (CPSs), Internet of Things (IoT), Internet of Services, big data analysis, artificial intelligence (AI), cloud computing, semantic web, and Augmented Reality (AR) [1]. Recently, they have been adapted by multiple manufacturers to resolve the challenges they are confronted with involving the increased demand for customized products with a short lead time on the market [2], as well as those related to meeting the needs of sustainable production in terms of waste and emission generation and the reduction in energy use [3]. Eventually, the implementation of smart factories will become a necessary option for the survival of manufacturers, regardless of the scale of their facilities and of their economic conditions. However, its high investment cost can be another challenge, especially for the operational management of small and medium-sized enterprises [4]. In general, smart factory technologies are applied to automation systems that replace traditionally manual tasks with machine- and robot-based operations, which include both physical aspects of manufacturing processes and their non-physical aspects, such as information processing and managerial decision-making [5]. Automation systems can be classified as follows by production quantity and variety: (1) programmable automation, enabling changes in the sequence of operations to process product configurations with a coded instruction program; (2) flexible automation, producing items without wasting time changing operations in other systems (i.e., fixed automation systems and programmable systems); and (3) fixed automation, utilizing equipment to automate a fixed sequence of either processing or assembly operations [6]. In these automation systems, industrial robots (IRs) are a key factor in the successful conversion from traditional manual tasks to automated tasks, with improved performance in reconfigurability, accuracy, productivity, versatility, repeatability, and safety [7]. According to the International Organization for Standardization (ISO) 8373:2021, an IR is defined as an \"automatically controlled, reprogrammable multipurpose manipulator, programmable in three or more axes, which can be either fixed in place or fixed to a mobile platform for use in automation applications in an industrial environment\" [8]. Among IRs, the articulated robot consisting of multiple interconnected links and joints is the most popular IR used in smart factories; because of its use of links and joints, it is capable of replicating the distinctive structural characteristics of human joints, enabling it to represent diverse motions [9]. Thus, articulated robots are well suited for a wide range of industrial applications, including the material handling, processing, assembly, and inspection operations of a product [6]. Picking tasks performed by IRs have been investigated by multiple researchers to achieve a high operational accuracy of IRs in industrial applications. [PERSON] et al. [10] proposed a joint learning methodology using semantic occlusion segmentation to enhance the reasoning instance process of a pick-and-place task. [PERSON] et al. [11] presented a raw RGB-D image-based framework generating a robot picking workspace while maintaining reasonable computational complexity. [PERSON] et al. [12] proposed an object-agnostic grasping system that processed observed images for different grasping actions. Meanwhile, [PERSON] et al. [13] applied an articulated robot arm under a flexible automation system to mimic a traditional manual rewinding process, and they found the devised automation system to be six times faster than the manual rewinding process. As an extension of the existing studies, this study aims to introduce a federated learning (FL)-based framework to train multiple articulated robots under a flexible automation system.