## 1 Introduction  
### Motivation and Problem Statement  
Applied artificial intelligence (AI) for real-time image processing applications in the industrial Internet of Things (IIoT) within 5G and beyond networks has the potential to gain popularity due to its remarkable performance outcomes [1, 2]. Advancements in end devices, the quality of data, sensing capabilities, and computing adequacy, together enable comprehensive services to achieve compelling performance targets. Although application services benefit from real-time considerations, the requirements for ultra-reliable low-latency performance are crucial. These requirements involve efficient utilization of support paradigms in both access and core networks, including new radio interfaces, green communications [3, 4], multiple-input multiple-output [5], software-defined networking (SDN) [6], multi-access edge computing (MEC) [7], network functions virtualization (NFV) [8], etc. Simultaneously, despite various enablers and framework supports, challenges persist within the IIoT, particularly in the era of big data generated from massive structured and unstructured data sources. These challenges include accommodating multiple types of vertical and horizontal services on a single platform, managing the heterogeneity of standard and proprietary devices, and handling extensive interfaces [9, 10]. Given these circumstances,the application of AI in the IIoT is motivated to extend into edge IIoT networks that aim to mitigate backbone congestion, enhance energy efficiency, and optimize the utilization of network resources. Edge AI enables intelligent applications through low-latency model learning and enhanced privacy protection. By combining MEC with deep learning (DL), the concept of edge intelligence is well established, particularly for industrial imaging and visual-based platforms. It offers agile multi-service responses and efficient utilization of resources in caching, communication, and computation [11; 12; 13]. However, the transmission of raw image/video data batches to the input gate significantly impairs fronthaul performance. Stringent privacy regulations and the inaccessibility of private data present challenges in accessing end-sensing data nodes. As a result, a privacy-enhanced framework becomes a crucial prerequisite to facilitate the advancement of edge AI model development. Federated learning (FL) was conceptualized in 2016 [14], offering a promising solution to tackle the challenges related to communication costs, data privacy, and regulatory compliance. Edge federated learning (EFL) in wireless IIoT communications represents a cutting-edge collaborative AI framework designed to address industrial image processing learning models [15]. By harnessing resources in close proximity to user equipment within distributed areas, EFL is explored to optimize battery consumption for local IIoT devices and mitigate the need for direct local-global round communications [16; 17; 18]. In the context of 5G core networks, 3 GPP and ETSI drive the utilization of MEC resources and interaction with network data analytics function (NWDAF) to facilitate AI-driven execution services. Through a service-based architecture, network functions and data exposure capabilities provide insightful information for comprehensive global network analytics. Additionally, supported by SDN/NFV-enabled systems, the network application programming interface (API) streamlines request procedures [19; 20; 21; 22; 23]. Nevertheless, despite these advancements, the diversity among EFL-based service types gives rise to challenges. The tasks of prioritizing service classes, harmonizing update aggregation policies, and accurately predicting congestion become intricate issues demanding real-time solutions. Regarding congestion predictions, ML/DL-based methods are given significant consideration.