9 papers
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…
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…
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,…
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…
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…
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.…