Recent developments in Artificial Intelligence techniques have enabled their successful application across a spectrum of commercial and industrial settings. However, these techniques require large volumes of data to be aggregated in a centralized manner, forestalling their applicability to scenarios wherein the data is sensitive or the cost of data transmission is prohibitive. Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation. To advance the adoption of Federated Learning, more research and development needs to be conducted to address some important open questions. In this work, we propose OpenFed, an open-source software framework for end-to-end Federated Learning. OpenFed reduces the barrier to entry for both researchers and downstream users of Federated Learning by the targeted removal of existing pain points. For researchers, OpenFed provides a framework wherein new methods can be easily implemented and fairly evaluated against an extensive suite of benchmarks. For downstream users, OpenFed allows Federated Learning to be plug and play within different subject-matter contexts, removing the need for deep expertise in Federated Learning.
翻译:人工智能技术的近期发展使其在一系列商业和工业环境中得以成功应用,然而,这些技术要求以集中方式汇总大量数据,防止其适用于敏感数据或数据传输费用过高的情景; 联邦学习联合会通过分散示范培训来缓解这些问题,从而消除数据传输和汇总的需要; 为推动采用联邦学习联合会,需要开展更多的研究和开发工作,以解决一些重要的未决问题; 在这项工作中,我们提议建立一个开放源软件框架,用于端到端的联邦学习; 通过有针对性地消除现有疼痛点,减少联邦学习研究人员和下游用户进入联邦学习联合会的障碍; 对研究人员来说,开放基金提供了一个框架,便于采用新的方法,并根据一套广泛的基准进行公平的评估; 对下游用户来说,开放基金允许联邦学习组织在不同的主题范围内插插和玩耍,从而消除了对联邦学习联合会深入专业知识的需求。