3 citations · 10 across the 17 of their papers we have counts for
9 papers · 1 filter
Trajectory-Aware Information Matching for Multi-Step Gradient Inversion in Federated Learning
Li Xia, Jing Yu, Zheng Liu +3
Federated learning enables distributed information sharing and collaborative model training without exposing raw client data. However, shared gradients or model updates may still c…
A Vision-Language Pre-training Model-Guided Approach for Mitigating Backdoor Attacks in Federated Learning
Keke Gai, Dongjue Wang, Jing Yu +2
Defending backdoor attacks in Federated Learning (FL) under heterogeneous client data distributions encounters limitations balancing effectiveness and privacy-preserving, while mos…
LLAMA: Multi-Feedback Smart Contract Fuzzing Framework with LLM-Guided Seed Generation
Keke Gai, Haochen Liang, Jing Yu +2
Smart contracts play a pivotal role in blockchain ecosystems, and fuzzing remains an important approach to securing smart contracts. Even though mutation scheduling is a key factor…
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection
Jianbo Gao, Keke Gai, Jing Yu +2
Recent advancement in large-scale Artificial Intelligence (AI) models offering multimodal services have become foundational in AI systems, making them prime targets for model theft…
PCDiff: Proactive Control for Ownership Protection in Diffusion Models with Watermark Compatibility
Keke Gai, Ziyue Shen, Jing Yu +2
With the growing demand for protecting the intellectual property (IP) of text-to-image diffusion models, we propose PCDiff -- a proactive access control framework that redefines mo…
Secure and Efficient Watermarking for Latent Diffusion Models in Model Distribution Scenarios
Liangqi Lei, Keke Gai, Jing Yu +2
Latent diffusion models have exhibited considerable potential in generative tasks. Watermarking is considered to be an alternative to safeguard the copyright of generative models a…
Vertical Federated Continual Learning via Evolving Prototype Knowledge
Shuo Wang, Keke Gai, Jing Yu +2
Vertical Federated Learning (VFL) has garnered significant attention as a privacy-preserving machine learning framework for sample-aligned feature federation. However, traditional…
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks
Keke Gai, Mohan Wang, Jing Yu +2
Multimodal Federated Learning (MFL) with mixed modalities enables unimodal and multimodal clients to collaboratively train models while ensuring clients' privacy. As a representati…
SLVC-DIDA: Signature-less Verifiable Credential-based Issuer-hiding and Multi-party Authentication for Decentralized Identity
Tianxiu Xie, Keke Gai, Jing Yu +2
As an emerging paradigm in digital identity, Decentralized Identity (DID) appears advantages over traditional identity management methods in a variety of aspects, e.g., enhancing u…