When we chose which For selecting the frameworks to test, we set some requirementsseveral factors were taken into account:* ML frameworks flexibility:The adaptability of the framework to manage different ML frameworks.* Licensing: It is mandatory that the framework has an open-source, permissive licensinglicense to cope with the typical requirements of real-world use cases.* Repository rating and releases: Rating in a repository is important for a FL framework as it indicates a high level of community interest and support, potentially leading to more contributions and improvements. Meanwhile, the first and latest releases indicate respectively the maturity and the support of the framework and whether it is released or still in a beta version.* Documentation and tutorials: The provided documentation with related tutorials has to be complete and well-made.* Readiness for commercial usage: The readiness of the framework to be developed in a real-world scenario. In order to establish the readiness, it was checked the version of the framework and the license.According to the previous criteria, an initial list including the most promising FL frameworks was completed. It comprised of the following products:* NVIDIA FL Application Runtime Environment (NVFlare)* Federated AI Technology Enabler (FATE)* Flower* PySyft* TBDIBM Federated Learning (IBMFL)* OpenFL* TensorFlowFederated (TFF)* FedML.The limitation in selecting only eighth FL frameworks arises from the evolving nature of the field. As a relatively recent and rapidly evolving technique, FL continues to witness the emergence of various frameworks, each with its unique features and capabilities. In this context, the choice to focus on these frameworks reflects the attempt to capture the current state of the art and provide an analysis of the most prominent and well-established options available. This selection aims to offer valuable insights into the leading frameworks that are currently considered among the best choices in the evolving landscape of FL. In the next sections, the aforementioned factors will be treated individ- ually, justifying the reasons behind the discard of some frameworks rather than others.