254 citations · 375 across the 6 of their papers we have counts for
7 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…
BFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction
Collin Meese, Hang Chen, Syed Ali Asif +3
Accurate real-time traffic flow prediction can be leveraged to relieve traffic congestion and associated negative impacts. The existing centralized deep learning methodologies have…
P-CFT: A Privacy-preserving and Crash Fault Tolerant Consensus Algorithm for Permissioned Blockchains
Wanxin Li, Collin Meese, Mark Nejad +1
Consensus algorithms play a critical role in blockchains and directly impact their performance. During consensus processing, nodes need to validate and order the pending transactio…