Structured Federated Learning for Personalised Intelligence on Devices

该项目旨在开发一个新的结构化联邦机器学习框架，以增强移动的和智能设备上人工智能的定制。它旨在使用户能够在其设备上接收定制服务，而无需将其敏感的个人数据发送给云服务提供商。预期的好处包括更高的隐私、数据安全性和设备性能，以及更好的最终用户体验。这项研究的预期成果包括新的知识，工具包和算法，用于开发基于机器学习的安全，高效和容错技术，用于软件应用程序，移动的服务，云计算，自动驾驶汽车和先进的制造工艺。

The project aims to develop a new structured federated machine-learning framework to enhance the customisation of artificial intelligence across mobile and smart devices. It seeks to enable users to receive customised services on their devices without sending their sensitive personal data to a cloud service provider. Anticipated benefits include greater privacy, data security and device performance, as well as better end-user experience. Expected outcomes of this research include new knowledge, toolkits and algorithms for use in developing machine-learning based secure, efficient and fault-tolerant technologies for software applications, mobile services, cloud computing, autonomous vehicles and advanced manufacturing processes.