5 papers
Risk-Aware Human-in-the-Loop Framework with Adaptive Intrusion Response for Autonomous Vehicles
Dawood Wasif, Terrence J. Moore, Seunghyun Yoon +4
Autonomous vehicles must remain safe and effective when encountering rare long-tailed scenarios or cyber-physical intrusions during driving. We present RAIL, a risk-aware human-in-…
MURIM: Multidimensional Reputation-based Incentive Mechanism for Federated Learning
Sindhuja Madabushi, Dawood Wasif, Jin-Hee Cho
Federated Learning (FL) has emerged as a leading privacy-preserving machine learning paradigm, enabling participants to share model updates instead of raw data. However, FL continu…
Multi-Agent Code-Orchestrated Generation for Reliable Infrastructure-as-Code
Rana Nameer Hussain Khan, Dawood Wasif, Jin-Hee Cho +1
The increasing complexity of cloud-native infrastructure has made Infrastructure-as-Code (IaC) essential for reproducible and scalable deployments. While large language models (LLM…
Advancing Human-Machine Teaming: Concepts, Challenges, and Applications
Dian Chen, Han Jun Yoon, Zelin Wan +9
Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust cali…
Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI
Dawood Wasif, Dian Chen, Sindhuja Madabushi +3
Federated Learning (FL) enables collaborative model training while preserving data privacy; however, balancing privacy preservation (PP) and fairness poses significant challenges.…