39 citations · 44 across the 11 of their papers we have counts for
6 papers · 1 filter
Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning
Chaoyu Zhang, Shanghao Shi, Heng Jin +3
Federated learning (FL) has become a foundational paradigm for multi-institutional medical AI, allowing hospitals and research centers to jointly train diagnostic models without ex…
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…
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…
Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats
Chaoyu Zhang, Heng Jin, Shanghao Shi +4
Federated Learning (FL) has gained significant attention for its privacy-preserving capabilities, enabling distributed devices to collaboratively train a global model without shari…
ProFLingo: A Fingerprinting-based Intellectual Property Protection Scheme for Large Language Models
Heng Jin, Chaoyu Zhang, Shanghao Shi +2
Large language models (LLMs) have attracted significant attention in recent years. Due to their "Large" nature, training LLMs from scratch consumes immense computational resources.…