# Federated Learning-Based Service Caching in Multi-Access Edge Computing System
## 5 Experiment and Evaluation
### Experimental Setup  
Extensive experiments are conducted to evaluate the performance of the proposed model. The simulation environment is an 8 km by 8 km residential area divided into 64 (\(1\times 1\)) units [6]. A total of 64 MEC servers are randomly distributed within each unit in this area. The computational capacity of each MEC server is set to 36 GHz. A backhaul network connects all the MEC servers to the cloud server. The connection between the cloud and MEC server has a bandwidth of 150 Mbps [26]. Moreover, MEC and cloud servers provide a category of 500 distinct services. Without compromising the generality, these services are listed in descending order of popularity.  
Within the simulation area, 300 users traverse the coverage areas of various MEC servers. The simulated user-position data are derived from the mobility traces of taxis in the central region of Rome, Italy, in 2014 [6]. This information contains the user ID, longitude, and latitude of each user in each time slot. The first ten days of mobility trace data are used to conduct the experiments. The user sends one request to the local MEC server during each 2-min time slot. The data size for each request is randomly generated following the uniform distribution in the range \([0.05,5]\) Mb. Furthermore, the required computational resources of the service are randomly generated in the range \([0.1,1]\) GHz. Notably, the distribution of requested service categories over the entire time slots follows a Zipf distribution, with an ordered list of 500 system-provided services. The parameter gamma for skewness is set to 0.8 [27].  
The neural network model used in this study is structured as follows. As stated in Section 4, the input and output dimensions of the autoencoder network are equivalent to the total number of services inside the system. Hence, the dimensions of both the input and output layers are set to 500. Based on the results mentioned in [28], it has been shown that the size of the latent layer is calculated using the equation \([1+\sqrt{size_{input}}]\), resulting in a value of 23. Thus, the first layer of discriminator is set to 23, whereas the final layer is 1. The sigmoid function facilitates the discriminator's ability to classify incoming samples from the specified distribution and the latent space. The learning rate used in the model training process is set to \(10^{-4}\). To enhance the efficiency of the model, the weights of the AE network are initialized according to the approach suggested by [29]. The ADAM optimization method [30] is used to train the model.  
The dataset, which consists of mobility traces and service requests from all users, is partitioned into training and testing sets in a 7:3 ratio based on the duration of the data samples. Each MEC server systematically acquires the training dataset at regular intervals of 60 min, referred to as the operation time slots. Following the preparation, the data are trained using individual MEC servers acting as clients in the FL paradigm. The local models are sent to the cloud server for aggregation and broadcast to the MEC servers. The communication rounds continue until the completion of the training phase. The trained model is used to determine the service caching decisions for individual servers. The performance of the approach is evaluated using metrics based on the services deployed and those requested by users in the testing set. Subsequently, the system efficacy is evaluated using metrics. Details of the parameters settings are listed in Table 1.