5 papers
AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning
Shuaijun Chen, Omid Tavallaie, Niousha Nazemi +2
As data volumes expand rapidly, distributed machine learning has become essential for addressing the growing computational demands of modern AI systems. However, training models in…
RBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaS
Shuaijun Chen, Omid Tavallaie, Niousha Nazemi +1
Federated Learning (FL) is a promising privacy-aware distributed learning framework that can be deployed on various devices, such as mobile phones, desktops, and devices equipped w…
ACCESS-FL: Agile Communication and Computation for Efficient Secure Aggregation in Stable Federated Learning Networks
Niousha Nazemi, Omid Tavallaie, Shuaijun Chen +5
Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client's data…
Analysis of DNS Dependencies and their Security Implications in Australia: A Comparative Study of General and Indigenous Populations
Niousha Nazemi, Omid Tavallaie, Anna Maria Mandalari +3
This paper investigates the impact of internet centralization on DNS provisioning, particularly its effects on vulnerable populations such as the indigenous people of Australia. We…
Boosting Communication Efficiency of Federated Learning's Secure Aggregation
Niousha Nazemi, Omid Tavallaie, Shuaijun Chen +2
Federated Learning (FL) is a decentralized machine learning approach where client devices train models locally and send them to a server that performs aggregation to generate a glo…