# A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Targets** & **Research Work** & **Contribution** & **Limitation** \\ \hline \multirow{4}{*}{Signature-based} & [PERSON] et al. [17] & Using NSRA to select nodes with high reputation prediction value. & Selecting nodes just by the historical reputation. \\ \cline{2-4}  & [PERSON] et al. [18] & Optimizing the selection of clients through label information of each cluster. & Obtaining such cluster labels may not always be feasible or may introduce additional computational overhead. \\ \cline{2-4}  & [PERSON] et al. [19] & Applying FedShift to alleviate the negative impact of class imbalance and improve the accuracy of the model. & The approach’s applicability to other forms of non-IID data needs to be further explored. \\ \hline \multirow{4}{*}{Fairness} & [PERSON] et al. [20] & Designing an incentive mechanism to remove low-quality clients and ensure a fair reward distribution. & In practice, the cost may not be easily accessible and the utility can be determined by multiple factors. \\ \cline{2-4}  & [PERSON] et al. [21] & Utilizing FHN to make clients to personalize the fairness metric enforced during local training freely. & Not all fairness metrics can be formalized. \\ \cline{2-4}  & [PERSON] et al. [22] & Employing E3 CS as selection decision under joint consideration of effective participation and fairness. & Real-time contributions of individual clients are not taken into account in the design of fairness factors. \\ \hline \multirow{4}{*}{Training time} & [PERSON] et al. [23] & Minimize the training latency without harming the ability of the model to generalize through MAB-based approach. & Lack of consideration for client heterogeneity. \\ \cline{2-4}  & [PERSON] et al. [24] & Propose MDA to speed up the learning process. & The generalization capability of the strategy across different FL scenarios should be assessed. \\ \cline{2-4}  & [PERSON] et al. [25] & Accelerating the convergence of the FL model using FGFL & When the data size is large, the scalability is not good. \\ \hline \multirow{4}{*}{Multi-Objective} & [PERSON] et al. [26] & Applying GAN to address the conflicting objectives of training time and energy consumption. & Approach has limited generalization to diverse scenarios. \\ \cline{2-4}  & [PERSON] et al. [27] & Minimize the MASE of the aggregation and maximize the long-term energy efficiency of the system via DRL-based framework. & Lack of comprehensive performance evaluation. \\ \cline{2-4}  & [PERSON] et al. [28] & Maximize the processing power, memory, and bandwidth capacity of devices by Fed-MOODS. & Lack of simulations with a large number of devices. \\ \hline \multirow{4}{*}{[PERSON] et al. [29]} & FedMGDA+ is proposed to guarantee fairness among users and robustness against malicious attackers. & Absence of privacy guarantees to ensure the privacy of user data. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of different IoV intrusion detection methods. While the previous studies focus on single-objective node selection, recent research has shifted towards addressing multi-objective node selection. In [26], the authors optimize a multi-objective problem that contains bandwidth and computing resource allocation to obtain a trade-off between the training time and energy consumption. In [27], the authors propose a deep reinforcement learning-based framework to minimize the AMSE of the over-the-air aggregation for different communication rounds and maximize the long-term energy efficiency of the system. In [28], the authors propose Fed-MOODS, a Multi-Objective Optimization-based Device Selection approach that significantly improves the model's convergence and performance. In [29], motivated by ensuring fairness and robustness, the authors formulate federated learning as multi-objective optimization and propose a new algorithm, FedMGDA+.