Experiments were run both in the local and cloud environments. Detailed results are illustrated in [1]. In essence, the results are very similar for both frameworks. Thus, they can be considered equivalent from the point of view of the metrics considered. It is also very important to note that for all the results regarding the cloud environment, there are '''very similar values between the testbed based on virtual machines and the one based on embedded devices'''. This is not obvious because moving from virtualized, x86-powered clients to ARM64-powered clients entails several issues that can affect the results of the FL application [34]. Among these, it is worth to remember the following:* Limited hardware resources: Embedded devices often have limited hardware resources, such as CPU, memory and computing power. This restriction can affect the performance of FL, especially if models are complex or operations require many resources.* Hardware variations: Embedded devices may have hardware variations between them, even if they belong to the same class. These hardware differences may lead to different behaviors in FL models, requiring more robustness in adapting to different devices.* Variations in workload: Embedded device applications may have very different workloads from those simulated in a virtual environment. These variations may lead to different requirements for FL.The fact that, despite this the frameworks perform well, highlights thematurity and readiness for commercial use of the frameworks.