## 5 Simulation Experiments
### Evaluation Results and Analysis  
The experiment initially compared the cache hit efficiency of the LFDC algorithm with other algorithms. As shown in Figure 4a, with the increase in global epoch numbers (i.e., online training/online caching time), the LFDC exhibited improved caching performance. Learning-based algorithms initially underperformed compared to traditional cache optimization algorithms, but they gradually demonstrated better cache performance with ongoing online training. In contrast, the cache hit rates of FIFO, LFU, and LRU fluctuated over time and, due to their inability to adapt to the environment, had lower cache hit efficiency than learning-based algorithms. LFDC essentially converged at 300 global epochs, while other learning algorithms converged earlier, which is attributed to the fact that some BSs did not participate in training in each epoch. Due to the optimization of the federated process, the system cache hit efficiency of LFDC had a clear advantage over other learning algorithms. Starting from the 100 th epoch, the average cache hit efficiency of LFDC was 23.87%, 12.51%, 13.66%, 55.27%, 64.36%, and 38.03% higher compared to Central DRL, distributed DRL, FedAvg, FIFO, LFU, and LRU, respectively. As shown in Figure 4b, as the number of UEs increases, the number of content requests also increases. The proposed LFDC algorithm likewise outperforms other algorithms. However, in Figure 4a,b, the curve trends are contrary. This is because, under the fixed cache capacity of the BS, as the number of less-popular new request content increases, a large portion of the new request content is not replaced by the local BS. Figure 5 compares the training energy consumption of LFDC with distributed DRL and FedAvg. As the number of epochs increases, LFDC exhibits superior energy performance compared to the other two learning algorithms. This is because, firstly, FedAvg \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline \(T\) & 1000 & Number of global epoch \\ \(L_{0}\) & 10 & Number of initial local iterations \\ \(\epsilon_{n}\) & 20 cycles/bit & CPU cycles per bit for training one data sample \\ \(fre\) & 4 GHz & Computation capacity of BSs \\ \(P_{n}\) & 500 W & Transmit power of \(BS_{n}\) \\ \(B\) & 20 MHz & Channel bandwidth of BSs downlink \\ \(v_{a}\) & 1000 Mbps & Transmission speed between cloud and BSs \\ \(v_{b}\) & 1 Gbps & Transmission speed between BSs \\ \(D_{f}\) & 10 Mbbit & Content size \\ \(\zeta_{n}\) & \(1.2\times 10^{-28}\) & Effective capacitance coefficient of \(BS_{n}\) \\ \(\varphi_{n}\) & \(1\times 10^{4}\) bit & Parameter size of model \\ \(\gamma\) & 0.9 & Discount factor \\ \(\eta\) & 0.01 & Step size \\ \(\epsilon\) & 0.1 & State transition probability \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter values. Figure 4: Average cache hit efficiency for different algorithms with (**a**) epoch (time) increasing, (**b**) number of UEs increasing. requires all BSs to continuously undergo online training and constantly upload their parameters. Although distributed DRL does not involve parameter uploading, its BSs are always engaged in online training, and the number of local training iterations remains constant. The LFDC method, by selecting suitable BSs for training, choosing appropriate parameters from these BSs for uploading, and dynamically adjusting the number of local training iterations, achieves better online training energy efficiency than the other learning algorithms. Figure 6 investigates the HE gain of the proposed LFDC, where \(T\) increases from 20 to 1000. Overall, after 1000 epochs, the proposed LFDC achieves the highest HE gain of 1.988439791 compared to the existing distributed DRL (1.078518039) and FedAvg (0.947555). This is because, in the environment deploying the LFDC algorithm, BSs obtain the experiences of other BSs while catering to local request characteristics. Furthermore, the dynamic federated strategy reduces the training energy consumption.