Some data can be even more sensitive than mobile keyboard interactions. Personal healthcare data must be managed with the highest consideration for privacy and security. However, this data is also invaluable to develop new useful AI applications, and FL offers a technical solution to that challenge: Hospitals can now collaborate with their data, such as medical imaging for automated cancer diagnostic systems, so researchers can use more significant databases capturing the largest spectrum of cases and pathological patterns. In the UK, NVIDIA is partnering with King’s College London and Owkin to create afederated learning platform for the National Health Service, that enables algorithms to travel from one hospital to another, training on local datasets.
某些数据甚至比移动键盘交互更为敏感。 个人医疗数据必须在高度重视隐私和安全性的前提下进行管理。 但是,这些数据对于开发新的有用的AI应用程序也是无价的,FL提供了应对这一挑战的技术解决方案:医院现在可以与其数据进行协作,例如用于自动癌症诊断系统的医学成像,因此研究人员可以使用更重要的数据库来捕获最大范围的病例和病理模式。 在英国,NVIDIA与伦敦国王学院和Owkin合作,为National Health Service创建了联邦学习平台 ,该平台使算法能够从一所医院转移到另一所医院,并接受本地数据集的培训。
Data intensive applications are another promising domain of application for FL: self-driving cars onboard many ML technologies to operate that rely on a lot of sensing data, such as computer vision with cameras or Lidar technologies. Federated learning can provide a solution to limit the volume of data transfer needed while allowing for real-time, continuous improvement for these applications, compared to classic centralised cloud approaches. This is an example of distributed edge computing application.
数据密集型应用是FL的另一个有前途的应用领域:许多ML技术的自动驾驶汽车都依靠大量传感数据进行操作,例如带摄像头的计算机视觉或激光雷达技术。 与传统的集中式云方法相比,联合学习可以提供一种解决方案,以限制所需的数据传输量,同时允许这些应用程序进行实时,持续的改进。 这是分布式边缘计算应用程序的示例。
More generally, since FL enables collaboration without actually requiring the transfer of data, it opens opportunities for cooperation between organisations. Problems shared across an industry are good candidates for utilising FL to support the development of more robust and effective machine learning solutions. Sectors likely to gain from such collaborations include supply chain, manufacturing, energy distribution, transport. The data collection burden can be spread between the participating organisations while they collectively benefit from improved solutions. This opens the opportunity for new business models for exploiting and managing access to data.
更一般而言,由于FL使协作成为可能,而实际上并不需要进行数据传输,因此为组织之间的合作提供了机会。 跨行业存在的问题是利用FL支持更健壮和有效的机器学习解决方案开发的良好候选者。 此类合作中可能受益的行业包括供应链,制造,能源分配,运输。 数据收集负担可以分散在参与组织之间,而它们可以共同受益于改进的解决方案。 这为开发和管理对数据的访问的新业务模型提供了机会。