Privacy protection is an ethical issue with broad concern in Artificial Intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across users or organisations without direct access to the data. It has great potential to be the next-general AI model training framework that offers privacy protection and therefore has broad implications for the future of digital health and healthcare informatics. Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organisations by building a next-generation collaborative framework with partner organisations and the research community. In particular, this game-changing collaborative framework offers knowledge sharing from diverse data with a privacy-preserving. This chapter will discuss how federated learning can enable the development of an open health ecosystem with the support of AI. Existing challenges and solutions for federated learning will be discussed.
翻译:联邦学习是一种新的机器学习模式,用于学习用户或组织之间无法直接获取数据的共同模式,它极有可能成为下一个通用的AI示范培训框架,提供隐私保护,因此对数字健康和保健信息学的未来具有广泛影响。在保健行业实施开放创新框架,即开放健康,就是通过与伙伴组织和研究界建立下一代合作框架,加强保健相关组织的创新和创造能力。特别是,这个改变游戏的合作框架提供从各种数据分享知识,并保护隐私。本章将讨论联邦学习如何在AI的支持下发展开放的卫生生态系统。将讨论联邦学习的现有挑战和解决办法。