# A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning
## 5 Simulation Analysis
### Analysis of Results
Similar to the convergence in accuracy, the FL-DQN algorithm converges faster and has the smallest loss function value than the remaining three algorithms, while FL-Random, FL-Greedy, and Local Training always have higher loss function values due to the presence of malicious nodes. Comparing the above four sets of simulation results, it can be seen that FL-DQN always converges quickly to the highest accuracy with different numbers of malicious nodes and has the lowest loss function compared to FL-Random, FL-Greedy and Local Training. At the same time, for FL-DQN algorithm, the accuracy rate of 0.98 is maintained for both 20% and 40% of malicious nodes. Therefore, it can be concluded that the proposed method in this paper is remarkably robust. Figure 4c presents the loss function achieved by the four algorithms for different numbers of nodes. The FL-DQN algorithm achieves the highest accuracy when dealing  
Figure 4: Loss function experimental group. with multiple numbers of nodes. The difference between FL-Random and FL-Greedy loss functions is not significant. Figure 4d shows the loss function values of the four algorithms obtained at the end of training under the same conditions. Compared to FL-Random, FL-Greedy, and Local Training, FL-DQN can handle different proportions of malicious nodes to guarantee the quality of the whole model and thus obtain the minimum of the loss function. Figure 5 presents the latency of four algorithms under different conditions of changing the number of iterations, the number of nodes, and the proportion of malicious nodes. Figure 5a shows the changes in latency of four algorithms with 20% malicious nodes. Figure 5b shows the changes in latency of four algorithms with 40%malicious nodes. Compared to the remaining three algorithms, the FL-DQN algorithm is able to complete the training task faster and has the smallest variation in the time used per round. As shown in Figure 5c, the FL-DQN algorithm can guarantee low latency in dealing with various numbers of nodes, as it can effectively select high-quality training devices for model aggregation. Taking the number of nodes as 40, the latency values for the four algorithms are 11.3 s, 13.8 s, 17.4 s, and 16.4 s, respectively. The FL-DQN algorithm reduces the latency by 18%, 35%, and 31% compared to FL-Random, FL-Greedy, and Local Training, respectively. These results indicate that the proposed algorithm can efficiently complete FL training. Figure 5d presents the latency changes of four algorithms with fixed training rounds (30 rounds) and different percentages of malicious nodes (from 10% to 80%). From Figure 5d, it can be observed that even when facing a large number of malicious nodes, the FL-DQN algorithm can still complete the training task relatively quickly. The following section compares and validates four algorithms using the CIFAR dataset. Figure 6 illustrates the accuracy variations of the four algorithms with 20% malicious device nodes. (CIFAR) The CIFAR dataset requires significantly more training iterations than the MNIST dataset. When the number of iterations reaches 60, the accuracy of the models trained by the four algorithms stabilizes. The accuracies of FL-DQN, FL-Random, FL-Greedy, and Local Training stabilize at 0.80, 0.71, 0.68, and 0.58, respectively. The FL-DQN algorithm demonstrated good training performance in handling malicious nodes and differential data quality. Figure 5: Latency experimental group. Figure 7 illustrates the accuracy variations of the four algorithms with 20% malicious device nodes. (Fashion MNIST) The FL-DQN algorithm continued to demonstrate excellent training performance with higher accuracy compared to other algorithms. Figure 8 displays the loss function variations of the four algorithms with 20% malicious device nodes. (CIFAR) Figure 9 presents the loss functions of four algorithms with 20% malicious device nodes. (Fashion MNIST) The FL-DQN algorithm achieves faster convergence and has the smallest loss function value.