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.

联合学习工具是协作机器学习（ML）的一个很有前途的框架，也可以维护数据隐私;然而，它们对异构数据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，用于ML模型的协调训练，成功地桥接可变数据分布。提出的框架将是全球第一个可以使用因果知识来1）处理设备之间的数据异构性，2）解决只有一部分设备具有标记数据时的现实挑战。预期的成果和好处包括ML模型的基于能力的协作训练的理论基础和算法，同时更好地保护用户的数据隐私。