Major ARC Linkage funding announced for secure quantum machine learning
SPINE is thrilled to share announced ARC Linkage Project funding for LP250200550, a $568,955 project on privacy-aware secure quantum machine learning.
Latest news from the SPINE Research Group.
SPINE is thrilled to share announced ARC Linkage Project funding for LP250200550, a $568,955 project on privacy-aware secure quantum machine learning.
SPINE has received CSIRO iPhD support for research on AI-driven threat intelligence and Large Language Models for organisational cybersecurity.
SPINE has received CSIRO Industry PhD Program support for a secure, privacy-preserving, and trustworthy multi-agent interoperability framework.
Wathsara Daluwatta’s paper on SSFU for federated learning in 6G Internet of Things systems has been published in IEEE Internet of Things Journal.
Charitha Elvitigala’s paper on intent-driven dual-layer model pruning for energy-efficient hierarchical federated learning in IoT has been published in IEEE Internet of Things Journal.
Asitha Kottahachchi Kankanamge Don’s paper on Q-VFL for privacy-preserving medical AI has been published in Quantum Machine Intelligence.
Ziqi Wang’s paper on KGEES, an energy saving system with location privacy preservation in multi-access edge computing, has been published in IEEE Transactions on Dependable and Secure Computing.
Mengsha Kou’s paper on data flipping attack and defense in web edge caching systems has been published in IEEE Transactions on Information Forensics and Security.
Ziqi Wang’s paper on MoSEEC for sustainable and trajectory privacy-preserving edge resource management has been published in IEEE Transactions on Mobile Computing.
Shehan Edirimannage’s paper on SecHeto-FL for heterogeneous IoT networks has been published in IEEE Transactions on Network Science and Engineering.
Mengsha Kou’s paper on WinFLoRA for client-adaptive aggregation in federated LoRA under privacy heterogeneity has been published at WWW 2026.
Amani Aldahiri’s paper on privacy-preserving class imbalance mitigation for face recognition has been accepted in High-Confidence Computing.