Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the field of medicine. In fact, the idea of machine learning in AI without collecting data from local clients is very attractive because data remain in local sites. However, federated learning techniques still have various open issues due to its own characteristics such as non identical distribution, client participation management, and vulnerable environments. In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The framework will be open to public after development completes.
翻译:自2017年Google采用联合学习,使AI在不移动当地数据的情况下学习成为可能以来,在2017年由Google引入了AI学习,特别是在医学领域,事实上,不从当地客户收集数据而在AI中进行机器学习的想法非常有吸引力,因为数据仍然留在当地;然而,由于联合会学习技术本身的特性,如分布不完全、客户参与管理和脆弱环境,因此仍然存在着各种尚未解决的问题。在本次介绍中,将简要概述当前使联合学习在现实世界中毫无用处的问题,这些问题涉及数据/系统差异、客户管理、可追踪性和安全。此外,我们引入模块化联合学习框架,我们目前正在开发,以试验各种技术和协议,为上述问题找到解决办法。这个框架将在发展完成后向公众开放。