Causal Knowledge-Empowered Adaptive Federated Learning

因果知识赋能的自适应联邦学习

• 批准号: DP240102088
• 负责人: Dr Mingming Gong
• 金额: $ 35.02万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP240102088
• 关键词: Causal; Knowledge; Empowered; Adaptive; Federated

Federated learning tools are a promising framework for collaborative machine learning (ML) that also maintain data privacy; however, their ability to model heterogeneous data remains a key challenge. This project aims to develop a new learning scheme for coordinated training of ML models that successfully bridges variable data distributions. The framework proposed will be the first globally that can use causal knowledge to 1) handle data heterogeneity across devices and 2) address the real-world challenges when only a subset of devices have labelled data. Expected outcomes and benefits include the theoretical underpinnings and algorithms of causality-based collaborative training of ML models while better preserving the users’ data privacy.

联合学习工具是一种很有前景的协作机器学习 (ML) 框架，同时还能维护数据隐私；然而，它们对异构数据进行建模的能力仍然是一个关键挑战，该项目旨在开发一种新的学习方案，以成功协调训练 ML 模型。所提出的框架将是全球第一个能够使用因果知识来 1）处理跨设备的数据异构性以及 2）解决只有一部分设备具有标记数据时的现实挑战的框架。理论的基于因果关系的机器学习模型协作训练的基础和算法，同时更好地保护用户的数据隐私。