TaRDIS-APIs/Artificial-Intelligence-and-Machine-Learning-APIs.md

@@ -122,6 +122,8 @@ Inference is a crucial component in federated learning, enabling the generation
 
 The usage of this API is simple, as it only requires selecting the trained model, the test data set and the type of the inference or evaluation. The output of this tool depends on the selection and may contain predictions, applied anomaly detection, forecasting or the application of different metrics.
 
+**References:**
+
 [1] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, A. Singh and J. Zhu, Eds., ser. Proceedings of Machine Learning Research, vol. 54, PMLR, 20–22 Apr 2017, pp. 1273–1282. [Online]. Available: https://proceedings.mlr.press/v54/mcmahan17a.html
 
 [2] C. T. Dinh, N. Tran, and J. Nguyen, “Personalized federated learning with moreau envelopes,” in Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33, Curran Associates, Inc., 2020, pp. 21 394–21 405. [Online]. Available: 	https://proceedings.neurips.cc/paper_files/paper/2020/file/f4f1f13c8289ac1b1ee0ff176b56fc60-Paper.pdf. 
@@ -130,7 +132,7 @@ The usage of this API is simple, as it only requires selecting the trained model
 
 [4] M. Savic, J. Atanasijevic, D. Jakovetic, N. Krejic, Tax evasion risk management using a Hybrid Unsupervised Outlier Detection method. Expert Syst. Appl., 2022,  https://doi.org/10.48550/arXiv.2103.01033
 
-[5] A. Ghosh, J. Chung, D. Yin and K. Ramchandran, "An Efficient Framework for Clustered Federated Learning," in IEEE Transactions on Information Theory, vol. 68, no. 12, pp. 8076-8091, Dec. 2022, doi: 10.1109/TIT.2022.3192506.
+[5] A. Ghosh, J. Chung, D. Yin and K. Ramchandran, "An Efficient Framework for Clustered Federated Learning," in IEEE Transactions on Information Theory, vol. 68, no. 12, pp. 8076-8091, Dec. 2022, https://doi.org/10.1109/TIT.2022.3192506.
 
 
 ### 3\. Fedra: A decentralized federated learning framework enabling secure P2P model training on edge devices