5 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…
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…