838 citations · 906 across the 27 of their papers we have counts for
13 papers · 1 filter
zkSAS: Practical Zero-Knowledge Proofs for Verifiable Spectrum Access Management
Nishat F. Purbasha, Ifteher Alom, Eric W. Burger +3
Dynamic Spectrum Access (DSA) through the Spectrum Access Systems (SAS) elevates spectral efficiency, yet existing centralized models face allocation logic opaqueness and a lack of…
Privacy-Preserving Split Learning for Federated LLM Fine-Tuning
Heng Jin, Chaoyu Zhang, Hexuan Yu +2
Fine-tuning large language models (LLMs) on domain-specific data is essential for downstream adaptation. In many deployments, a participant cannot hold the complete model locally.…
Skynet: Workflow-Level Anomaly Detection for Agentic AI via Semantic and Structural Modeling
Chaoyu Zhang, Hexuan Yu, Heng Jin +6
Agentic AI systems execute complex tasks through long-horizon workflows of planning, tool use, and multi-agent coordination. Task failures in these systems often originate from a s…
Enabling Emergency Communication via Semantic Radar-Centric ISAC
Mohaimin Al Barat, Chaoyu Zhang, Hexuan Yu +3
Combat aircraft and other modern military platforms field two co-located RF assets: a radar for sensing and a dedicated radio for communication. In contested battlefield environmen…
SpexPay: A Privacy-Preserving Pay-As-You-Go System for Dynamic Spectrum Sharing
Mohaimin Al Barat, Hexuan Yu, Shaoyu Li +5
Dynamic Spectrum Sharing (DSS) is a cornerstone of next-generation wireless systems, yet existing solutions such as Spectrum Access Systems (SAS) rely on centralized administrators…
From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning
Shanghao Shi, Chaoyu Zhang, Heng Jin +6
Federated learning (FL) enables multiple parties to collaboratively fine-tune language models for domain-specific tasks without sharing raw data. Since full model fine-tuning is of…
Minim: Privacy-Aware Minimal View for Agents via Trusted Local Sanitization
Hexuan Yu, Chaoyu Zhang, Heng Jin +4
Modern LLM-powered autonomous agents increasingly rely on rich user interface (UI) state observations to achieve reliable action grounding in complex digital environments. However,…
Demystifying Deep Reinforcement Learning: A Neuro-Symbolic Framework for Interpretable Open RAN Automation
Jie Lu, Peihao Yan, Pang-Ning Tan +2
Open Radio Access Networks (O-RAN) are increasingly adopting data-driven control through Deep Reinforcement Learning (DRL) to optimize complex tasks such as network slicing and mob…
ARMOR 2025: A Military-Aligned Benchmark for Evaluating Large Language Model Safety Beyond Civilian Contexts
Sydney Johns, Heng Jin, Chaoyu Zhang +2
Large language models (LLMs) are now being explored for defense applications that require reliable and legally compliant decision support. They also hold significant potential to e…
ClawCoin: An Agentic AI-Native Cryptocurrency for Decentralized Agent Economies
Shaoyu Li, Chaoyu Zhang, Hexuan Yu +2
Autonomous AI agents live or die by the API tokens they consume: without paid inference capacity they cannot reason, act, or delegate. Compute-token cost has become the binding res…
Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI
Heng Jin, Chaoyu Zhang, Hexuan Yu +4
Cloud-based infrastructure has become the dominant platform for deploying large models, particularly large language models (LLMs). Fine-tuning and inference are increasingly delega…
IU-GUARD: Privacy-Preserving Spectrum Coordination for Incumbent Users under Dynamic Spectrum Sharing
Shaoyu Li, Hexuan Yu, Shanghao Shi +4
With the growing demand for wireless spectrum, dynamic spectrum sharing (DSS) frameworks such as the Citizens Broadband Radio Service (CBRS) have emerged as practical solutions to…
FC-GUARD: Enabling Anonymous yet Compliant Fiat-to-Cryptocurrency Exchanges
Shaoyu Li, Hexuan Yu, Md Mohaimin Al Barat +3
With the rise of decentralized finance, fiat-to-cryptocurrency exchange platforms have become popular entry points into the cryptocurrency ecosystem. However, these platforms frequ…