Structured Federated Learning for Personalised Intelligence on Devices

用于设备上个性化智能的结构化联合学习

• 批准号: DE230100495
• 负责人: A/Prof Jing Jiang
• 金额: $ 29.75万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE230100495
• 关键词: Structured; Federated; Learning; Personalised; Intelligence

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.

该项目旨在开发一种新的结构化联邦机器学习框架，以增强跨移动和智能设备的人工智能定制。该公司试图让用户在不向云服务提供商发送敏感个人数据的情况下，在自己的设备上接收定制服务。预期的好处包括更好的隐私、数据安全和设备性能，以及更好的终端用户体验。这项研究的预期成果包括新知识、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、移动服务、云计算、自动驾驶汽车和先进制造工艺。