In recent years, federated learning (FL) has gained widespread attention as an effective solution to break down the barrier to data sharing and is being progressively applied in areas such as healthcare, finance, and smart cities. FL frameworks are the cornerstones of academic research and industrial applications. Although companies such as Google, OpenMined, WeBank, and Baidu have their own open-sourced FL frameworks and systems, there is a lack of in-depth research and comparison of the technical principles, applicability scenarios, and the problems of these FL open-source frameworks. For this reason, according to the preference level of each open-source framework in the industry, we select the widely used open-source frameworks to analyze. For the different types of FL frameworks, firstly, we analyze the system architecture and system function. Secondly, we compare and analyze each framework from the aspects of privacy mechanism, machine learning algorithm, computing paradigm, learning type, training architecture, communication protocol, visualization, etc. Moreover, we present two FL experiments for different application scenarios to help the readers choose and use the open-source framework to implement FL applications. Finally, based on the openness of the current framework, we discuss the possible future research directions from the aspects of privacy security, incentive mechanism, cross-framework interaction, etc. This paper aims to provide references and ideas for developing and innovating an open-source framework, architecture optimization, security improvement, and algorithm optimization.