Causal Knowledge-Empowered Adaptive Federated Learning

联邦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的挑战。该项目旨在开发一种新的学习方案，用于成功地桥接可变数据分布的机器学习模型的协调训练。提出的框架将是全球第一个可以使用因果知识来处理设备间数据异质性的框架，以及2)在只有一小部分设备具有标记数据时解决现实世界的挑战的框架。预期的结果和收益包括基于因果关系的机器学习模型协同训练的理论基础和算法，同时更好地保护用户的数据隐私。

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.