Title: Causal Knowledge-Empowered Adaptive Federated Learning. 
Project ID: DP240102088	
Investigators: Dr Mingming Gong; Professor Howard Bondell; Professor Rajkumar Buyya; Associate Professor Kun Zhang	

Abstract: Causal Knowledge-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.	

Funding: $506,145.00

Source: https://rms.arc.gov.au/RMS/Report/Download/Report/a3f6be6e-33f7-4fb5-98a6-7526aaa184cf/259