## 6 Conclusions  
This paper presents an approach integrating a multi-branch network with the Oort client selection algorithm. Our proposed method includes incorporating a Model Distributor module, which efficiently clusters clients and dispatches appropriate models to them. Through extensive experimentation, we have demonstrated the effectiveness of our approach in mitigating the impact of non-iid data, which is not considered in the MFedAvg method. Furthermore, our approach surpasses the performance of the original Oort paper.  
Additionally, we introduced the concept of uniformity, which provides a straightforward measure of the training time gap among participants and identifies the presence of stragglers. The concept of uniformity offers valuable insights into the distribution of training time and facilitates the assessment of fairness and efficiency in the federated learning process.  
Our results showcase the benefits of integrating a multi-branch network and the Oort client selection algorithm. Furthermore, we have emphasized the significance of considering uniformity in designing and evaluating federated learning frameworks.  
In the future, we will conduct further investigations to enhance the overall accuracy of our approach. We plan to explore various techniques, including model architecture modifications, optimization algorithms, and the incorporation of additional data preprocessing methods. Moreover, we will conduct experiments on larger datasets to gain insights into the scalability and generalizability of our approach. We aim to refine and optimize our approach by undertaking these efforts, ultimately achieving higher accuracy and better performance in federated learning. Moreover, as mentioned in Section 3.4, FL plays crucial roles in edge-computing- and IoT-related applications; investigating the applicability of our approach in wireless communication environments is of great interest and will undoubtedly be one of our future research directions.  
Finally, as one of the anonymized reviewers pointed out, this work took the bandwidth of the communication channel as one of the criteria for selecting clients; however, the channel characteristics do affect the adequate bandwidth, too. The anonymized reviewer also suggested we consider replacing our current bandwidth measure with the client's achievable data rate. In response to this precious comment, we are searching for practical  
Figure 10: The uniformity measures under different values of \(\mu\).  
datasets (such as those applied to wireless edge FL scenarios) and planning to re-conduct our experiments shortly.  
**Author Contributions:** Formal analysis, P.-H.J.; Funding acquisition, J.-L.W.; Investigation, P.-H.J. and J.-L.W.; Methodology, P.-H.J.; Project administration, J.-L.W.; Resources, J.-L.W.; Software, P.-H.J.; Supervision, J.-L.W.; Writing--original draft, P.-H.J.; Writing--review & editing, J.-L.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially supported by the Minister of Science and Technology, Taiwan (grant number: MOST 111-2221-E-002-134-MY3), National Taiwan University (grant number: NTU-112L90902), and Taiwan Semiconductor Manufacturing (grant number: TSMC 112H1002-D).  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.