2 citations · 3 across the 12 of their papers we have counts for
12 papers
Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems
Rongping Zhou, Omid Tavallaie, Shuaijun Chen +1
Reinforcement learning (RL) is commonly employed to enhance the performance of autonomous systems, including the Autonomous Internet of Things (AIoT). However, the trial-and-error…
Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Distributed Fine-Tuning
Xin Chen, Shuaijun Chen, Omid Tavallaie +3
Federated Learning (FL) enables collaborative model training across decentralized data sources while preserving data privacy. However, the growing size of Machine Learning (ML) mod…
Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
Seunghyun Lee, Omid Tavallaie, Shuaijun Chen +4
Accommodating edge networks between IoT devices and the cloud server in Hierarchical Federated Learning (HFL) enhances communication efficiency without compromising data privacy. H…
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…
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks
Omid Tavallaie, Kanchana Thilakarathna, Suranga Seneviratne +2
Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Ind…
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…
Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Models
Wentao Gao, Omid Tavallaie, Shuaijun Chen +1
Federated learning (FL) is a distributed Machine Learning (ML) framework that is capable of training a new global model by aggregating clients' locally trained models without shari…
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…
Optimization of Federated Learning's Client Selection for Non-IID Data Based on Grey Relational Analysis
Shuaijun Chen, Omid Tavallaie, Michael Henri Hambali +5
Federated learning (FL) is a novel distributed learning framework designed for applications with privacy-sensitive data. Without sharing data, FL trains local models on individual…
GT-TSCH: Game-Theoretic Distributed TSCH Scheduler for Low-Power IoT Networks
Omid Tavallaie, Seid Miad Zandavi, Hamed Haddadi +1
Time-Slotted Channel Hopping (TSCH) is a synchronous medium access mode of the IEEE 802.15.4e standard designed for providing low-latency and highly-reliable end-to-end communicati…