12 papers · 1 filter
Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
Baolei Zhang, Minghong Fang, Zhuqing Liu +5
Federated Learning (FL) allows multiple clients to collaboratively train a model without sharing their private data. However, FL is vulnerable to Byzantine attacks, where adversari…
Secure Retrieval-Augmented Generation against Poisoning Attacks
Zirui Cheng, Jikai Sun, Anjun Gao +4
Large language models (LLMs) have transformed natural language processing (NLP), enabling applications from content generation to decision support. Retrieval-Augmented Generation (…
Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation
Baolei Zhang, Haoran Xin, Yuxi Chen +6
Retrieval-Augmented Generation (RAG) integrates external knowledge into large language models to improve response quality. However, recent work has shown that RAG systems are highl…
Fairness-Constrained Optimization Attack in Federated Learning
Harsh Kasyap, Minghong Fang, Zhuqing Liu +2
Federated learning (FL) is a privacy-preserving machine learning technique that facilitates collaboration among participants across demographics. FL enables model sharing, while re…
Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach
Yueyang Quan, Chang Wang, Shengjie Zhai +2
Decentralized min-max optimization allows multi-agent systems to collaboratively solve global min-max optimization problems by facilitating the exchange of model updates among neig…
Benchmarking Poisoning Attacks against Retrieval-Augmented Generation
Baolei Zhang, Haoran Xin, Jiatong Li +5
Retrieval-Augmented Generation (RAG) has proven effective in mitigating hallucinations in large language models by incorporating external knowledge during inference. However, this…