## 2 Related Work  
The concept of federated learning was first presented by Google in 2017. This model facilitates the training of machine-learning models without centralized data. Instead, user data are stored on the client's side, and all clients participate in the training process. The primary objective of federated learning is to safeguard user privacy and achieve a more generalized model. During training, client-side processing exclusively handles user data, and the model gradient undergoes encryption upon returning to the server to prevent access by other clients. Furthermore, in federated learning, the server-side aggregation algorithm considers all client neural network model parameters to yield a more generalized model. [PERSON] et al. proposed a Federated Averaging (FedAvg) algorithm to aggregate client model parameters on the server side in federated learning [13]. The FedAvg algorithm computes the average of the client model parameters and employs them as a global model for a specific round. The main distinction between federated and traditional deep-learning methods is the dataset environment. In traditional deep learning, the dataset is centralized to provide the model with access to all data. However, in federated learning, clients independently own their datasets, resulting in various data distributions and impacting the model performance. Based on the state of the client dataset, federated learning can be categorized into independent and identically distributed (IID) or non-IID. Non-IID datasets reduce the effectiveness of federated learning owing to client drift, as explained by [PERSON] et al. [14]. When the client datasets are non-IIDs, the local models of each client are updated in different directions, resulting in conflicting effects during aggregation on the server and decreasing the effectiveness of federated learning. [PERSON] et al. (2020) also suggested that client data heterogeneity could cause client drift, reducing the efficiency and accuracy of federated learning [15]. [PERSON] et al. utilized reinforcement learning to minimize the effects of non-IID datasets on a global model and increase the convergence rate of federated learning [16]. [PERSON] et al. investigated and analyzed the performance of the FedAvg algorithm in an environment where the client was distributed with non-IID datasets [17]. The study found that the severity of non-IID datasets significantly affected the performance of the models trained using FedAvg. [PERSON] et al. proposed the FedProx algorithm to enhance the vulnerability of the FedAvg algorithm to heterogeneous client data by modifying the training objective function of the client model [18]. Personalized federated learning is a novel machine-learning technique that addresses the issue of data heterogeneity in federated learning. In traditional federated learning, a global model is shared among all clients; however, when the data are non-uniformly distributed among clients, the efficacy of the approach is compromised. Personalized federated learning creates an individualized model for each client that adapts to the heterogeneity of their datasets, thereby enhancing the effectiveness of the federated learning. [PERSON] et al. examined the limitations of traditional federated learning when dealing with various forms of heterogeneity and suggested that personalized federated learning can effectively resolve the heterogeneity issue [19]. FedTP developed a learn-to-personalize mechanism to encourage cooperation among clients and to increase scalability and generalization. The results show that FedTP performed better than FedAvg-T in all cases. FedTP can combine the FedPer, FedRod, and KNN-Per methods to enhance model performance [20]. FedOFA, is design with utilizing personalized orthogonal filter attention for parameter recalibration. And FedOFA also only operates on the server side, incurring no additional computational cost on the client, making it advantageous in communication-constrained scenarios [21].