# A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning
## 6 Conclusions  
In this paper, we propose a novel multi-objective node selection approach that leverages deep reinforcement learning. Our proposed approach can efficiently solve MOP and provides an efficient solution for FL node selection. First, we construct a model that fully takes into account device training delay, model transmission delay, and accuracy rate to optimize node selection. Then, we formulate the problem as an MDP model and design a node selection algorithm based on the DQN algorithm to select a reasonable set of devices for model aggregation before each training iteration. Finally, the simulation results demonstrate that the proposed approach significantly improves the accuracy and training speed of federated learning while maintaining good resistance to malicious nodes. However, it is important to note that further research is needed to address the limitations related to optimizing the aggregation process while considering communication overhead. In future work, we plan to explore the application of our algorithm in real-world scenarios, where factors such as network conditions, device heterogeneity, and varying data distributions may pose additional challenges. In addition, we aim to enhance our node selection strategy by considering factors such as node reputation and reliability. By incorporating these additional factors, we can further optimize the node selection process and improve the overall performance of federated learning.  
Figure 8: Comparison of loss functions for CIFAR dataset.  
Figure 9: Comparison of loss functions for Fashion MNIST dataset.  
**Author Contributions:** Software, T.X. and Z.M.; Validation, Y.L. and P.L.; Investigation, T.X.; Resources, Y.H.; Data curation, Y.L.; Writing--original draft, T.X. and Y.H.; Writing--review and editing, Z.M.; Supervision, P.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Heilongjiang Province of China (No. LH2022F003), and the Key Research and Development Program Heilongjiang Province of China (No. 2022 ZX01A35).  
**Data Availability Statement:** The MNIST dataset used in this study is publicly available and can be accessed from the official website of the Modified National Institute of Standards and Technology (MNIST) at [[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)). The CIFAR-10 dataset used in this study is publicly available and can be accessed from the official website of the Canadian Institute for Advanced Research (CIFAR) at [[https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)]([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)). The Fashion MNIST dataset used in this study is publicly available and can be accessed from the official website of the Zalando Research at [[https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)]([https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)).  
**Conflicts of Interest:** The authors declare no conflict of interest.