# Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions
## 6 Conclusions  
In this work, we propose a federated learning pipeline that allows artificial intelligence experts and medical institutions to join forces in the development of collaborative deep learning models. The proposed solution, the Notebook Federator, demonstrates the promising results of an automated federated learning framework built from Jupyter and Docker ecosystems to simplify its use by bridging the gap between artificial intelligence developers and the Jupyter environment, as well as reducing the complexity of FL systems' deployment. As this first implementation started from an initial scenario based on several assumptions, our future lines of work will focus on deploying this framework to all collaborators of the CLARIFY medical federation presented in this paper while prioritizing the security aspect by the inclusion of differential privacy and blockchain methods into the workflow.  
Conceptualization, L.L., Y.W., A.C., V.N. and Z.Z.; methodology, L.L., Y.W., C.P.-O. and Z.Z.; medical data recollection and preparation, A.M.-Z. and C.M.; medical data annotation, A.M.-Z.; model preparation and data curation, L.L.; software, L.L. and Y.W.; system, Y.W.; Jupyter notebook-based toolbox updates and maintenance, S.K. and R.B.; visualization: L.L. and Y.W.; writing--original draft preparation, L.L. and Y.W.; writing--medical review and editing, C.M. and A.M.-Z.; writing--review and editing, Z.Z., V.N., A.C. and J.I.; supervision, Z.Z., V.N. and A.C.; project administration: Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by the European Union's Horizon 2020 research and innovation programme with the project CLARIFY under [PERSON] (860627), ENVRI-FAIR (824068), BlueCloud (862409), and ARTICONF (825134). This work is also supported by LifeWatch ERIC, GVA through projects PROMETEO/2019/109 and INNEST/2021/321 (SAMUEL), and the Spanish Ministry of Economy and Competitiveness through project PID2019-105142 RB-C21 (AI4 SKIN). The work of [PERSON] has been supported by the ValgrAI - Valencian Graduate School and Research Network for Artificial Intelligence & Generalitat Valenciana and Universitat Politecnica de Valencia (PAID-PD-22).  
The spitzoid melanocytic lesions dataset was collected at the Pathology Laboratory of the University Clinic Hospital of Valencia (Valencia, Spain). This study was carried out in accordance with the Declaration of Helsinki, and approved by the hospital's Ethics Committee under the approval number 2020/114.  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.  
We gratefully acknowledge the support from the Generalitat Valenciana (GVA) with the donation of the DGX A100 used for this work, action co-financed by the European Union through the Operational Program of the European Regional Development Fund of the Comunitat Valenciana 2014-2020 (IDIFEDER/2020/030).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
ACC Accuracy  
AI Artificial Intelligence  
DOAJ Directory of Open Access Journals  
FIS F1-score  
FL Federated Learning  
MDPI Multidisciplinary Digital Publishing Institute  
NaaVRE Notebook-as-a-VRE  
NPV Negative Predictive Value  
PPV Positive Predictive Value  
ROI Region of Interest  
SN Sensitivity  
SPC Specificity  
VRE Virtual Research Environment  
WSI Whole Slide Image