## 6 Conclusions  
The overall results presented in this article indicate that training a model using a P2P FL architecture could be a viable approach for collaborative neural network modeling among multiple clients without sharing their training data. Firstly, the results show that models trained with FedAVGP2P and FedSGD2P are comparable to models trained with the centralized FedAVG architecture in terms of accuracy. FedP2P may be less desirable due to higher global network costs compared to FedAVG, as more data need to be transmitted to achieve comparable model convergence behaviors. However, the use of a P2P topology offers several advantages, such as the absence of a single point of failure and dependence on a central server. This makes P2P FL a wise choice if these characteristics are required.  
As future work, it would be interesting to investigate whether these clusters emerge by analyzing the choice of neighbors for each client throughout the training process. It would also be valuable to explore the scenarios in which FedAVGP2P or FedSGD2P would be faster than FedAVG, taking into account the training time. The answer to this question depends on various factors, such as communication constraints and client systems.  
For instance, using FedAVG could be a faster approach if the central server has sufficient bandwidth. However, FedAVGP2P could also be faster if the central server lacks such bandwidth. Looking at certain curves related to the heuristics of the FedAVGP2P and FedSGD2P algorithms, we observe the influence of the f1-score achieved based on the number of rounds and the fraction of clients. This indicates the possibility of studying the trade-off between precision and communication according to the methods used.  
Furthermore, at a fixed precision level, the different methods yield varying numbers of rounds, which can be utilized to measure the communication cost of each method. Similarly, we can explore and compare the methods to find the one that achieves the best precision at a fixed communication cost.  
To further refine the relevance of our results, additional measurements should be implemented by increasing the number of collaborative clients. Our results in P2P FL, through FedAVGP2P and FedSGD2P, demonstrate it as a promising approach for training neural network models across multiple clients. The experiments conducted in this paper and the subsequent results clearly show that there are options for ensuring the confidentiality of data in a medical setup, where massive and sensitive data are needed to have an optimized model. Security features, in terms of privacy, can further be added by exploring the possibilities offered by homomorphic encryption.  
Conceptualization, M.S.A.T., M.H., S.D. and K.T.; Methodology, S.D. and K.T.; Software, V.P. and A.K.D.; Validation, M.S.A.T. and M.H.; Formal analysis, M.S.A.T.; Resources, V.P.; Writing--original draft, M.S.A.T.; Writing--review & editing, M.S.A.T., M.H., S.D. and K.T.; Supervision, D.S. All authors have read and agreed to the published version of the manuscript.  
The Research was funded by XLIM, UMR CNRS 7252, University of Limoges.  
Not applicable.  
Data Availability Statement:Data are contained within the article.  
**Acknowledgments:** We would like to thank the research team \"U1094 Inserm U270 IRD EpiMaCT Epidemiologie des maladies chroniques en zone tropicale\" of University of Limoges, which has contributed to this work by offering the processing power of their server for the GPU processing.  
**Conflicts of Interest:** The authors declare no conflict of interest.