Undertake two projects to develop scalable, differentially private approaches for distributed machine learning.
An opportunity for two talented students to undertake their PhDs on two projects that concentrate on developing differentially private approaches and blockchain-based solutions for privacy-preserving trustworthy distributed machine learning.
In this research, the successful candidates will focus on mathematical backgrounds involved in differential privacy to devise novel scalable approaches suitable for the privacy preservation of distributed machine learning scenarios such as federated learning, split learning, and distributed stochastic gradient descent. The existing differentially private machine learning approaches show issues in model performance and efficiency and vulnerability towards the application of noise over a large number of distributed models. These issues should be overcome by developing robust and feasible mathematical models for differential privacy by investigating the data dynamics (IID and Non-IID) of distributed machine learning. Besides, trustworthiness is another major property that needs to be investigated in distributed machine learning. Blockchain-based approaches are gaining much attention due to the inherent data immutability and trustworthiness properties offered by the underlying architectures. However, the high latency introduced by blockchain makes timely machine learning decision-making challenging. Hence, in this research, the students will also conduct thorough research in implementing feasible blockchain-based solutions to support trustworthy machine learning.
Up to $32,841 per year for 3 years as scholarship. The scholarship may be extended to 3.5 years depending on the progress.
This scholarship provides:
To be eligible for this scholarship, you must:
14 September 2024.
Acknowledgement of Country
RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Luwaytini' by Mark Cleaver, Palawa.