## 5 Conclusions  
In order to solve the data draft problem in federated learning in non-IID datasets, this study proposed a personalized federated learning algorithm based on the concept of  
Figure 5: The accuracy rates for various datasets: (**a**) Fashion-MNIST with \(\alpha=1\); (**b**) Fashion-MNIST with \(\alpha=0.1\); (**c**) Fashion-MNIST with \(\alpha=0.05\); (**d**) CIFAR10 with \(\alpha=1\); (**e**) CIFAR10 with \(\alpha=0.1\); (**f**) CIFAR10 with \(\alpha=0.05\); (**g**) CIFAR100 with \(\alpha=1\); (**h**) CIFAR100 with \(\alpha=0.1\); (**i**) CIFAR100 with \(\alpha=0.05\).  
multitask learning, which divides client models into feature extraction layers and category prediction layers. The model category prediction layer is trained using only the client data to maintain the characteristics of each client's data, whereas the feature extraction layer is trained using all clients. In addition, model aggregation is limited based on the difference between the client model of the previous round and the global model. In this study, three image datasets, Fashion-MNIST, CIFAR-10, and CIFAR-100, were used, and the data were divided into non-IID datasets of different degrees. The experimental results showed that under a non-IID data distribution, the proposed federated learning algorithm converges within five rounds and achieves a 0.5 data loss on the CIFAR-10 dataset. It converges within 20 rounds and achieves a 0.1 data loss on the CIFAR-100 datasets. Unlike previous federated learning, this study plans and classifies each customer's data characteristics to find the personalized weight. The proposed method has a faster convergence speed and lower data loss than the FedProx and FedPer federated learning algorithms for all three datasets, demonstrating its effectiveness in non-IID data distributions.  
Overall, this study provides a new federated learning algorithm that can effectively train deep-learning models while protecting personal privacy. However, category prediction is modeled by the distribution of the number of data categories for each client without considering the relevant data features and number differences. The results of this study are mainly limited to the distribution of classes with similar data features and cannot be inferred when the data features are very different. In the future, the performance evaluations of diverse client data with heterogeneity could be an extension of this study.  
Conceptualization, Y.-H.L. and H.-C.C.; methodology, Y.-H.L.; software, H.-Y.H.; validation, S.-Y.C.; formal analysis, S.-Y.C.; investigation, W.-C.C.; resources, Y.-H.L.; data curation, W.-C.C.; writing--original draft preparation, Y.-H.L.; writing--review and editing, H.-C.C.; visualization, H.-C.C.; supervision, H.-C.C.; project administration, H.-C.C. and H.-Y.H.; funding acquisition, H.-C.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to client privacy.  
Author [PERSON] was employed by the Taiwan Semiconductor Manufacturing Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.