The broad application of artificial intelligence techniques ranging from self-driving vehicles to advanced medical diagnostics afford many benefits. Federated learning is a new breed of artificial intelligence, offering techniques to help bridge the gap between personal data protection and utilization for research and commercial deployment, especially in the use-cases where security and privacy are the key concerns. Here, we present OpenFed, an open-source software framework to simultaneously address the demands for data protection and utilization. In practice, OpenFed enables state-of-the-art model development in low-trust environments despite limited local data availability, which lays the groundwork for sustainable collaborative model development and commercial deployment by alleviating concerns of asset protection. In addition, OpenFed also provides an end-to-end toolkit to facilitate federated learning algorithm development as well as several benchmarks to fair performance comparison under diverse computing paradigms and configurations.
翻译:联邦学习是人工智能的新品种,提供了各种技术,帮助弥合个人数据保护和用于研究和商业部署,特别是在安全和隐私是主要关切事项的使用案件中,特别是在安全和隐私的使用案件中,我们在此介绍开放软件框架OpenFed,这是一个开放源码软件框架,可以同时满足数据保护和利用的需求。实际上,开放Fed使低信任环境中最先进的模型得以开发,尽管当地提供的数据有限,通过减轻对资产保护的关切,为可持续协作模型的开发和商业部署奠定了基础。此外,开放Fed还提供了一个端到端工具包,以促进联合学习算法的开发,以及在不同计算模式和配置下进行公平业绩比较的若干基准。