127 citations · 576 across the 102 of their papers we have counts for
112 papers
JABER: Sequential Quality Control for Human-AI Data Annotation under Shared Bias
Fengxu Yang, Minghui Liwang, Yang Lu +3
High-quality annotations are essential for training and evaluating artificial intelligence (AI) systems. Quality control (QC) often acquires additional human or AI judgments before…
BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models
Payam Abdisarabshali, Fardis Nadimi, Kasra Borazjani +3
Federated vision-language models (FedVLMs) represent one of the emerging frontiers of machine learning, aiming to integrate federated learning within the fine-tuning pipelines of v…
Fisher-Informed Recalibration for Feedback-Based On-Policy Self-Distillation of LLMs
Seohyun Lee, Dong-Jun Han, Seyyedali Hosseinalipour +1
Feedback-based on-policy self-distillation has emerged as a promising approach for enabling foundation models, more specifically Large Language Models (LLMs), to learn from their o…
Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment
Zhan-Lun Chang, Dong-Jun Han, Seyyedali Hosseinalipour +2
Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retrievers and rerankers optimize for…
HermesHFL: Incentive-Compatible Hierarchical Federated Unlearning for Dynamic LLM Fine-Tuning
Chenxi Sun, Minghui Liwang, Wusi He +5
Hierarchical federated unlearning (HFUL) for large language model (LLM) fine-tuning faces significant challenges due to hierarchical aggregation, dynamic client participation, and…
SAGE: A Socially-Aware Generative Engine for Heterogeneous Multi-Agent Navigation
Lan Hu, Minghui Liwang, Wenbo Zhu +5
Safe and socially compliant navigation in open human-robot environments requires robots to reason about heterogeneous participants with different dynamics, autonomy levels, and soc…
DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation
Yaoshen Yu, Minghui Liwang, Wenbo Zhu +5
Future intelligent transportation systems are envisioned to evolve toward a long-term mixed-autonomy paradigm, where human-driven vehicles (HVs) and autonomous vehicles (AVs) coexi…
STEPS: Semantic Contract-Guided Scheduling for LLM-Assisted Natural Language-Driven Edge AI Services
Houyi Qi, Minghui Liwang, Xianbin Wang +2
Edge user/service scheduling governs where and how AI services are executed under limited communication and computing resources. Existing edge scheduling frameworks typically assum…
Federated Foundation Models over Vehicular Networks
Kasra Borazjani, Fardis Nadimi, Payam Abdisarabshali +5
This paper presents a forward-looking vision for integrating the emerging multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular networks, with the goal of…
Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning
Seohyun Lee, Wenzhi Fang, Dong-Jun Han +2
Recent works have advanced feedback-based learning systems, whereby a foundation model is able to intake incoming feedback (e.g., from a user) to self-improve, creating a self-loop…
Spatiotemporal Link Formation Prediction in Social Learning Networks Using Graph Neural Networks
Ali Mohammadiasl, Bita Akram, Seyyedali Hosseinalipour +1
Social learning networks (SLNs) are graphical representations that capture student interactions within educational settings (e.g., a classroom), with nodes representing students an…
Look One Step Ahead: Forward-Looking Incentive Design with Strategic Privacy for Proactive Service Provisioning over Air-Ground Integrated Edge Networks
Sicheng Wu, Minghui Liwang, Yangyang Gao +5
In air-ground integrated networks (AGINs), unmanned aerial vehicles (UAVs) provide on-demand edge services to ground vehicles. Realizing this vision requires carefully designed inc…
RE-MCDF: Closed-Loop Multi-Expert LLM Reasoning for Knowledge-Grounded Clinical Diagnosis
Shaowei Shen, Xiaohong Yang, Jie Yang +4
Electronic medical records (EMRs), particularly in neurology, are inherently heterogeneous, sparse, and noisy, which poses significant challenges for large language models (LLMs) i…
Sporadic Gradient Tracking over Directed Graphs: A Theoretical Perspective on Decentralized Federated Learning
Shahryar Zehtabi, Dong-Jun Han, Seyyedali Hosseinalipour +1
Decentralized Federated Learning (DFL) enables clients with local data to collaborate in a peer-to-peer manner to train a generalized model. In this paper, we unify two branches of…
ELSA: Efficient LLM-Centric Split Aggregation for Privacy-Aware Hierarchical Federated Learning over the Network Edge
Xiaohong Yang, Tong Xie, Minghui Liwang +6
Training large language models (LLMs) in resource-constrained edge networks poses fundamental challenges in computation, communication, data heterogeneity, and privacy. To address…
FUSION: Forecast-Embedded Agent Scheduling with Service Incentive Optimization over Distributed Air-Ground Edge Networks
Houyi Qi, Minghui Liwang, Seyyedali Hosseinalipour +5
This paper introduces a forecasting-driven, incentive-aware service provisioning framework for distributed air--ground integrated networks with human--machine coexistence. Agent pa…
Game-Theoretic Safe Multi-Agent Motion Planning with Reachability Analysis for Dynamic and Uncertain Environments (Extended Version)
Wenbin Mai, Minghui Liwang, Xinlei Yi +3
Ensuring safe, robust, and scalable motion planning for multi-agent systems in dynamic and uncertain environments is a persistent challenge, driven by complex inter-agent interacti…
Voltage-Regulated Sparse Optimization for Proactive Diagnosis of Voltage Collapses
Qinghua Ma, Seyyedali Hosseinalipour, Ming Shi +2
This paper aims to proactively diagnose and manage the voltage collapse risks, i.e., the risk of bus voltages violating the safe operational bounds, which can be caused by extreme…
Frequency-Aware Sparse Optimization for Diagnosing Grid Instabilities and Collapses
Swadesh Vhakta, Denis Osipov, Reetam Sen Biswas +3
This paper aims to proactively diagnose and manage frequency instability risks from a steady-state perspective, without the need for derivative-dependent transient modeling. Specif…
From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks
Allan Salihovic, Payam Abdisarabshali, Michael Langberg +1
We provide our perspective on X-Learning (XL), a novel distributed learning architecture that generalizes and extends the concept of decentralization. Our goal is to present a visi…
Auctioning Future Services in Edge Networks with Moving Vehicles: N-Step Look-Ahead Contracts for Sustainable Resource Provision
Ziqi Ling, Minghui Liwang, Xianbin Wang +5
Timely resource allocation in edge-assisted vehicular networks is essential for compute-intensive services such as autonomous driving and navigation. However, vehicle mobility lead…
Synergies between Federated Foundation Models and Smart Power Grids
Seyyedali Hosseinalipour, Shimiao Li, Adedoyin Inaolaji +3
The recent emergence of large language models (LLMs) such as GPT-3 has marked a significant paradigm shift in machine learning. Trained on massive corpora of data, these models dem…
Federated Foundation Models in Harsh Wireless Environments: Prospects, Challenges, and Future Directions
Evan Chen, Seyyedali Hosseinalipour, Christopher G. Brinton +1
Foundation models (FMs) have shown remarkable capabilities in generalized intelligence, multimodal understanding, and adaptive learning across a wide range of domains. However, the…
Empowering AI-Native 6G Wireless Networks with Quantum Federated Learning
Shaba Shaon, Md Raihan Uddin, Dinh C. Nguyen +3
AI-native 6G networks are envisioned to tightly embed artificial intelligence (AI) into the wireless ecosystem, enabling real-time, personalized, and privacy-preserving intelligenc…