* ===== Local environment: =====The local parties consist of a single desktop computer that acts both as the server and four separate clients. This configuration mimics a decentralized environment where the desktop computer takes on the roles of multiple participants, simulating the interactions and data contributions of distinct entities. As the server, it coordinates and manages the FL process, while functioning as an individual client allows it to provide diverse data contributions for training. This localized approach allowed for the use of Docker as the development environment, leveraging the power of a desktop computer assembled with an RTX 3080ti GPU to enhance performance. The power given by the NVidia GPU, allowed the use of a more complex model and the simulation of four clients on the same machine that acts also as the server. Being self-contained in a single host, the local environment is convenient for testing, especially when the focus is on the functional verification.* ===== Cloud environment: =====In this case, cloud parties consist of two embedded devices or virtual machines acting as clients, and a notebook serving as the server. This configuration facilitates a distributed learning approach, enabling the clients to process their data locally while contributing to the model’s training. The server coordinates the learning process and aggregates the updates from the clients to improve the global model. This setup ensures a decentralized and privacy-preserving approach to ML, as the data remains on the clients’ devices, and only the model updates are shared during the training process. Leveraging embedded devices as clients enables the inclusion of resource-constrained devices in the FL ecosystem, making the framework more versatile and applicable to a wide range of scenarios. The notebook acting as the server provides a centralized point of coordination and ensures smooth communication and collaboration between the clients, making the FL process efficient and effective in leveraging distributed resources for improved model performance. Of course, this environment is more complicated to set up, but it better simulates real configurations.

Another important feature characteristic to consider the ease of use of a framework. This is an important a relevant aspect because it influences the practicality and circulation of a framework. Rereference Reference [1] illustrates the comparison between NVFlare and Flower with respect to the following relevant items concerning the ease of use:* Core Architecture

* SupportFeature support* Technical Supportsupport.