* '''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 license 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.