# Federated Learning-Based Service Caching in Multi-Access Edge Computing System
## 6 Conclusions  
In this study, we propose a distributed server caching framework to optimize the performance of the MEC system. The proposed method aims to maximize the service hit ratio while minimizing service latency. This is the result of the capability to select cache services that satisfy user requirements while taking into account the resources of the system. The integration of the FL framework and AAE model enables each edge server to acquire in-depth knowledge derived from both local characteristics and global data distributions. The proposed approach efficiently mitigates the limitations of centralized systems, such as network congestion, by using FL. SCFL also guarantees the security and privacy of user data by avoiding from sharing original data that may include sensitive information. In addition, SCFL effectively addresses the challenges encountered by existing distributed cache systems, including complexity, convergence challenges, and high computing resource requirements. The proposed framework efficiently captures the complex data distributions due to the dynamic nature of the MEC system by using the AAE model, while also ensuring simplicity and convergence. The results of intensive experiments demonstrate that the proposed framework performs better than other strategies in effectively caching suitable services. Using SCFL helps increase the service hit ratio from 1.25 to 5 times compared to its counterparts. Additionally, SCFL-using systems reduce service latency by 15-50%, demonstrating their ability to adapt to system conditions. Due to its simple nature, the integration of SCFL into current MEC infrastructures can be seamless, especially if the MEC system already accommodates machine learning operations. The connectivity between MEC servers and a central server allows for the formation of an FL architecture on the edge computing system. The AAE model is deployed at edge servers to capture local data distribution, while a central server is used to aggregate global knowledge received from the entire system. The MEC servers and the central server use a wired connection for the purpose of transferring models. Once the mixing knowledge is obtained, MEC servers conduct the inference process to provide cache scores for services. Each MEC server has the ability to independently make service cache decisions, taking into account the available resources and cache score. The proposed architecture guarantees  
Figure 11: Total delay with different numbers of MEC servers. scalability by integrating FL, which allows for the participation of various MEC servers without compromising the system's functioning. In the future, we aim to expand the scope of research by conducting a comprehensive evaluation of the system latency. Specifically, all factors causing system delay, including computation, model training, and transmission, are investigated to improve the practicality of the proposed method. Furthermore, issues raised by the FL framework, such as training asynchronously between MEC servers, also need to be considered. Conceptualization, A.H.N.T. ; Methodology, T.P.T., A.H.N.T. and T.M.N. ; Formal analysis, T.P.T. ; Writing--original draft, A.H.N.T. ; Writing--review and editing, T.P.T., T.M.N. and M.Y. ; Supervision, M.Y. ; Funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) funded by the Korean Government [Ministry of Science and ICT (MSIT)], South Korea, through the Development of Candidate Element Technology for Intelligent 6G Mobile Core Network under Grant 2022-0-01015; and in part by MSIT under the Information Technology Research Center (ITRC) Support Program Supervised by IITP under Grant IITP-2023-2021-0-02046. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to project and funding-related policies.