# Federated Learning-Based Service Caching in Multi-Access Edge Computing System
## 1 Introduction
Consequently, centralized decision-making strategies become insufficient as the scale of the system increases, particularly considering the constrained resources and latency-sensitive nature of the applications. In addition, the centralized caching strategy leads to concerns about security and privacy problems [14]. The centralization of all user data, including sensitive information such as location and service requests, might potentially serve as the primary goal of a cyberattack. Distributed service caching approaches have been proposed to address these issues. In distributed systems, the decision-making process of each MEC server is independent. It relies only on its state to determine appropriate services. This mitigates the stress on system resources and alleviates concerns about security vulnerabilities. Nevertheless, the efficiency of distributed techniques is compromised because of the constraints associated with local data. Specifically, owing to the dynamic nature of the system, using only local data at each MEC server to make decisions becomes ineffective. To address this issue, existing distributed service caching requires formulating complex optimization models. A previous study [15] adapted a system delay optimization model to the [PERSON] decision process and proposed a deep Q-network for optimal decision making. Similarly, ref. [16] formulated a nonlinear mixed-integer optimization problem and solved it using a meta-heuristic algorithm. However, existing distributed approaches face several challenges. Optimization problems are thoroughly formulated to represent the real-world environment. Reinforcement Learning (RL) algorithms often need a significant amount of interaction with the environment in order to acquire an efficient policy [17]. This inefficiency may pose a constraint, particularly in situations involving expensive or time-consuming data collection. Moreover, the state and action spaces are high dimensional, posing challenges for algorithms to efficiently explore and acquire information [17]. The curse of dimensionality may result in delayed learning and increased computational demands. Moreover, using these techniques in a distributed approach increases the complexity of the training process while simultaneously raising challenges in terms of convergence and stability [18]. In order to overcome these issues, we propose a distributed Service Caching Federated Learning-based framework (SCFL) with the objective of optimizing MEC system performance. SCFL utilizes a distributed strategy by storing and using data locally on MEC servers rather than gathering all data on a centralized server. This solution not only addresses the issue of optimizing system resources, but also mitigates problems related to security vulnerabilities. On the other hand, SCFL employs an autoencoder-based model for the purpose of acquiring data characteristics. The simplicity and efficiency of the proposed method not only guarantee optimal system performance, but also addresses the drawbacks associated with the complexity of existing distributed approaches. Specifically, the Adversarial Autoencoder (AAE) [19] model is utilized by MEC servers to capture complex data distributions based on local data. The Federated Learning (FL) [14] architecture is used to aggregate the local knowledge of MEC servers at a central server. This approach enables the system to acquire global knowledge about user request patterns without necessitating the sharing of the original data. Subsequently, the global model is sent to all MEC servers and is retrained using a limited quantity of local data before being utilized. This process enables the model to include global knowledge and adapt to the individual request patterns of each MEC server. Thus, the proposed method efficiently addresses the challenge arising from the dynamic nature of MEC systems. Based on mixing knowledge, each MEC server can make optimal cache service decisions independently.