# Federated Learning-Based Service Caching in Multi-Access Edge Computing System
## 1 Introduction  
Mobile networks have become integral to modern society, connecting people and devices to the internet anytime and anywhere. With the increasing popularity of resource-intensive applications such as virtual and augmented reality, voice control, video streaming, 3D modeling, and interactive games [1; 2; 3], mobile networks generate a tremendous amount of data traffic. Cloud computing is an ideal approach for managing these tasks owing to its extensive computational and storage resources. Nevertheless, this approach has significant disadvantages, including high delay, low scalability, and risk of network congestion. To address this challenge, Multi-access Edge Computing (MEC) [4] has emerged as a promising solution in response to these limitations. By transferring computational tasks closer to data sources at the network edge, MEC reduces latency, improves data throughput, and enables a more efficient utilization of network resources. However, the MEC system suffers from several problems arising from the inherent characteristics of the MEC environment. It is important to note that the computational resources available to MEC servers, which are embedded in base stations or access points, are much more constrained than those of cloud servers. Therefore, each MEC server deploys only a certain number of applications. The caching of appropriate services, commonlyreferred to as service caching, is an essential challenge in ensuring system performance. To address this issue, a static service caching approach was proposed by [5] to minimize the service response time. A fixed optimal decision is made without considering the dynamics of the user requests. Nevertheless, the ever-changing characteristics of MEC systems could result in a substantial influence on cache performance. Under real-life scenarios, such as in Rome, Italy [6], which is used as an experimental scenario, the geographical location of each user changes continuously over time. Consequently, each MEC server provides services to a different group of users at every time slot. These fluctuations lead to variations in the request patterns on each MEC server, leading to inconsistencies in the services that are requested and cached [7]. The caching service has to rapidly adapt to meet different request patterns. Moreover, the constantly changing workloads of services and resource-constrained MEC servers need effective caching solutions to reduce delay and enhance the overall quality of the user experience with available resources. Hence, employing a dynamic and adaptable approach to determine appropriate services is crucial, considering user demands and the geographical context. Several dynamic service caching techniques have been proposed to facilitate the adaptive configuration of MEC services [8; 9; 10; 11]. These approaches can be categorized into two types: centralized and distributed. In a centralized service caching system, a single central entity is responsible for making optimal decisions for the entire system. The central server collects data on user requests, resources available on MEC servers, and the transmission state. The data are then utilized as inputs for the optimal service caching decision-making procedure. In [8], the central server gathers information on the arrival of data and the conditions of the channel to dynamically allocate services. Nevertheless, this approach is constrained by the inherent limits of centralized systems. Initially, the process of gathering data on the condition of system resources and user request data is resource-intensive [12]. The system is negatively affected by the high amount of information, ongoing transmission process, and reliance on a single centralized point, resulting in challenges such as network congestion and connection interruptions. Moreover, the scalability of centralized methods is a significant concern [13]. The number of MEC servers in the system exhibits a direct correlation with the complexity of the optimization problem.