Traditional Federated Learning (FL) follows a server-domincated cooperation paradigm which narrows the application scenarios of FL and decreases the enthusiasm of data holders to participate. To fully unleash the potential of FL, we advocate rethinking the design of current FL frameworks and extending it to a more generalized concept: Open Federated Learning Platforms. We propose two reciprocal cooperation frameworks for FL to achieve this: query-based FL and contract-based FL. In this survey, we conduct a comprehensive review of the feasibility of constructing an open FL platform from both technical and legal perspectives. We begin by reviewing the definition of FL and summarizing its inherent limitations, including server-client coupling, low model reusability, and non-public. In the query-based FL platform, which is an open model sharing and reusing platform empowered by the community for model mining, we explore a wide range of valuable topics, including the availability of up-to-date model repositories for model querying, legal compliance analysis between different model licenses, and copyright issues and intellectual property protection in model reusing. In particular, we introduce a novel taxonomy to streamline the analysis of model license compatibility in FL studies that involve batch model reusing methods, including combination, amalgamation, distillation, and generation. This taxonomy provides a systematic framework for identifying the corresponding clauses of licenses and facilitates the identification of potential legal implications and restrictions when reusing models. Through this survey, we uncover the the current dilemmas faced by FL and advocate for the development of sustainable open FL platforms. We aim to provide guidance for establishing such platforms in the future, while identifying potential problems and challenges that need to be addressed.

翻译：传统联邦学习遵循以服务器为主导的合作范式，这限制了联邦学习的应用场景，并降低了数据持有者的参与积极性。为了充分释放联邦学习的潜力，我们主张重新审视当前联邦学习框架的设计，并将其扩展为更广义的概念：开放联邦学习平台。为此，我们提出两种互惠合作的联邦学习框架：基于查询的联邦学习和基于契约的联邦学习。本综述从技术和法律两个视角，系统性地评估了构建开放联邦学习平台的可行性。我们首先回顾联邦学习的定义，并总结其固有局限性，包括服务器-客户端耦合、模型复用性低以及非公开性。在基于查询的联邦学习平台中——这是一种由社区赋能、用于模型挖掘的开放模型共享与复用平台——我们探讨了诸多有价值的话题，包括用于模型查询的最新模型库的可用性、不同模型许可证之间的法律合规性分析，以及模型复用中的版权问题与知识产权保护。特别是，我们引入了一种新颖的分类法，以系统化地分析涉及批量模型复用方法（包括组合、融合、蒸馏和生成）的联邦学习研究中的模型许可证兼容性。该分类法提供了一个系统框架，用于识别许可证的相应条款，并有助于识别复用模型时潜在的法律影响和限制。通过本综述，我们揭示了联邦学习当前面临的困境，并倡导发展可持续的开放联邦学习平台。我们旨在为未来建立此类平台提供指导，同时识别需要解决的潜在问题与挑战。