# Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions
## 2 Related Work  
Although federated learning (FL) was initially developed for mobile and edge device use cases under a cross-device paradigm [9], cross-silo FL recently gained traction in various domains, including healthcare [10]. Under this setting, models are trained on siloed data across organizations, and medical data remains local and decentralized in each collaborating institution (client) [9]. Only the resulting weights and parameters are shared and aggregated together before being transferred back to the different clients. In recent years, these applications have become increasingly popular in the medical image analysis field, as shown in some successful implementations, including brain imagingclassification and segmentation [6; 11; 12], EEG signal classification [13], and, more recently, histopathology [14; 15]. [PERSON] et al. [6] demonstrated the feasibility of using a client-server FL system to perform brain tumor segmentation. [PERSON] et al. [11] introduced BrainTorrent, which is a peer-to-peer serverless FL environment, to perform a whole-brain segmentation. [PERSON] et al. [15] presented a large-scale computational pathology study to demonstrate the feasibility and effectiveness of privacy-preserving FL using thousands of gigapixel whole slide images from multiple institutions. However, due to the complexity of FL implementation in real-world situations and large-scale deployment, most of these medical applications of FL still rely on simulations. To support the different FL scenarios and simplify their deployment and execution process, some studies proposed easy-to-use open-source FL frameworks, such as PySyft [16], OpenFL [17], FLOWER [18] or Federated Scope [19]. The majority of these solutions focuses on the architecture of the FL framework. Whereas FLOWER [18] provides a core framework architecture with Edge Client Engine and Virtual Client Engine, Federated Scope [19] proposes an event-driven architecture with an asynchronous training protocol in FL for heterogeneity. These open-source frameworks have served as a basis to build federated setups, some of them in the medical field. In [20], [PERSON] et al. leveraged the PySyft framework to implement a thyroid ultrasound image classifier across six medical institutions and compared the performance of federated training with that of conventional centralized model training. In the same vein, [PERSON] et al. [21] made use of FLOWER to simulate an FL system for COVID-19 detection on CT images, where the clients were deployed locally on a single machine. In contrast, in a work on OCT imaging [22], although the authors did mention the existence of FL frameworks, they chose to simplify their implementation through an independent development and to simulate the clients' nodes locally on a single supercomputer cluster. Despite these available FL frameworks, there is no explicit pipeline for the FL training process between the framework and data scientists in these solutions, and most steps have to be done manually without FL automation. It is thus still time-consuming for domain-specific users to deploy their FL applications, not only because of the heterogeneous resources but also because of the complexity of models, such as software dependencies and parameters to deploy. Additionally, although many AI researchers make use of Jupyter notebooks to develop DL models, there is a clear gap between the Jupyter environment and FL systems. Although some specific frameworks do provide a version of the FL code presented in a Jupyter notebook [19], there is still a need to upload the data to the cloud, and these notebooks are not fully integrated into the FL pipeline. In fact, they are mostly based on simulations rather than real-world scenarios running on different clients' machines. Despite the many system construction efforts for FL frameworks, there is a clear need to simplify their deployment process and address the gap between Jupyter users and FL systems.