1 citations · 1 across the 15 of their papers we have counts for
15 papers
Human-Centric Intelligence in the Era of Foundation Models: A Survey
Yang Chen, Tianqi Wang, Xiaorui Jiang +13
Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, and general-purpose modeling. Yet it has not fully integrated w…
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…
STELLA: Efficient Sensor-to-LLM Translation for On-Device Human Activity Recognition
Nirhoshan Sivaroopan, Albert Zomaya, Kanchana Thilakarathna
HAR is increasingly expected to run continuously on edge devices, yet recent LLM-based methods remain hard to deploy: raw sensor prompts are long, cloud inference adds latency and…
HiRL: Hierarchical Reinforcement Learning for Coordinated Resource Management in Heterogeneous Edge Computing
Jianyong Zhu, Hao Chen, Juan Zhang +3
Edge computing faces unprecedented resource orchestration challenges from multi-dimensional heterogeneity across device architectures, diverse task requirements in CPU-intensive, G…
The Evolution of Decentralized Systems: From Gray's Framework to Blockchain and Beyond
Zhongli Dong, Young Choon Lee, Albert Y. Zomaya
Blockchain technology is often discussed as if it emerged from nowhere, yet its architectural DNA traces directly to the decentralized computing principles James~N. Gray articulate…
SHIELD: An Auto-Healing Agentic Defense Framework for LLM Resource Exhaustion Attacks
Nirhoshan Sivaroopan, Kanchana Thilakarathna, Albert Zomaya +6
Sponge attacks increasingly threaten LLM systems by inducing excessive computation and DoS. Existing defenses either rely on statistical filters that fail on semantically meaningfu…
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…
Falcon: Advancing Asynchronous BFT Consensus for Lower Latency and Enhanced Throughput
Xiaohai Dai, Chaozheng Ding, Wei Li +5
Asynchronous Byzantine Fault Tolerant (BFT) consensus protocols have garnered significant attention with the rise of blockchain technology. A typical asynchronous protocol is desig…
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…