254 citations · 733 across the 18 of their papers we have counts for
18 papers
Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation
Hang Chen, Collin Meese, Mark Nejad +1
Low-latency traffic prediction is vital for smart city traffic management. Federated Learning has emerged as a promising technique for Traffic Prediction (FLTP), offering several a…
B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction
Hao Guo, Collin Meese, Wanxin Li +2
Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distribu…
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Maryam Shaygan, Collin Meese, Wanxin Li +2
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of add…
Aggregated Zero-knowledge Proof and Blockchain-Empowered Authentication for Autonomous Truck Platooning
Wanxin Li, Collin Meese, Hao Guo +1
Platooning technologies enable trucks to drive cooperatively and automatically, providing benefits including less fuel consumption, greater road capacity, and safety. This paper in…
A Hybrid Blockchain-Edge Architecture for Electronic Health Records Management with Attribute-based Cryptographic Mechanisms
Hao Guo, Wanxin Li, Mark Nejad +1
This paper presents a hybrid blockchain-edge architecture for managing Electronic Health Records (EHRs) with attribute-based cryptographic mechanisms. The architecture introduces a…
A Hierarchical and Location-aware Consensus Protocol for IoT-Blockchain Applications
Hao Guo, Wanxin Li, Mark Nejad
Blockchain-based IoT systems can manage IoT devices and achieve a high level of data integrity, security, and provenance. However, incorporating existing consensus protocols in man…