# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 7 Conclusions  
A substantial amount of private data remains confined within clients, largely due to privacy and security concerns. Federated learning frameworks generally achieve high accuracy, but their performance may occasionally decline due to data heterogeneity, particularly when dealing with unbalanced and non-IID data distributions. This research aims to explore the impact of data heterogeneity, including quantity skew, label distribution skew, and image skew, on the performance of federated learning, particularly when working with limited data. The proposed work Fed-Hetero, a self-evaluating framework, recommends strategies for addressing data heterogeneity by first identifying the specific type of heterogeneity associated with client data. Fed-Hetero evaluates client performance and recommends techniques such as data augmentation or clustering to enhance outcomes. This ensures the inclusion of clients with limited data in the federated learning process. This study can be expanded to incorporate the feature distribution skew and analyze its impact on the performance of the federated learning setup. Transferring a subset of images to the server for estimating data skewness raises privacy concerns; therefore, we plan to explore privacy-preserving methods as part of our future work.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**FL Algorithm/ Approach** & **Ref** & **Quantity Skew** & **Label Skew** & **Image Skew** & **Feature Skew** \\ \hline one-pass distribution & [22] & ✓ & ✓ & & \\ \hline FedFed & [19] & ✓ & ✓ & & \\ \hline federated daisy chaining & [23] & ✓ & ✓ & & \\ \hline FedAF & [20] & & ✓ & & ✓ \\ \hline FedIcon & [21] & & & & ✓ \\ \hline FedZDA & [18] & & ✓ & & \\ \hline
**Fed-Hetero** & & ✓ & ✓ & ✓ & \\ \hline \hline \end{tabular}
\end{table}
Table 14: Comparison of existing works with Fed-Hetero.  
**Author Contributions:** Conceptualization, A.M.K. and A.J.; methodology, A.M.K.; software, A.M.K.; validation, A.J.; formal analysis, K.R., G.G., and A.J.; investigation, A.M.K. and A.J.; resources, A.M.K.; writing--original draft preparation, A.M.K.; writing--review and editing, A.M.K. and A.J.; visualization, A.M.K. and A.J.; supervision, K.R. and A.J.; project administration, K.R., G.G. and A.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The authors express their gratitude to the \"Artificial Intelligence for Health and Well-being\" Thrust Area Group within the Department of Computer Science and Engineering (Dept of CSE), Amrita School of Computing, as well as Amrita Vishwa Vidyapeetham, for their invaluable assistance and guidance throughout the study.  
**Conflicts of Interest:** The authors state that there are no conflicts of interest to disclose, and all authors have approved the manuscript and consent to its submission.