217 citations · 1.8k across the 154 of their papers we have counts for
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Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents
Yuqi Jia, Saeed Vahidian, Jingwei Sun +4
Data heterogeneity presents significant challenges for federated learning (FL). Recently, dataset distillation techniques have been introduced, and performed at the client level, t…
Mendata: A Framework to Purify Manipulated Training Data
Zonghao Huang, Neil Gong, Michael K. Reiter
Untrusted data used to train a model might have been manipulated to endow the learned model with hidden properties that the data contributor might later exploit. Data purification…
Competitive Advantage Attacks to Decentralized Federated Learning
Yuqi Jia, Minghong Fang, Neil Zhenqiang Gong
Decentralized federated learning (DFL) enables clients (e.g., hospitals and banks) to jointly train machine learning models without a central orchestration server. In each global t…
Formalizing and Benchmarking Prompt Injection Attacks and Defenses
Yupei Liu, Yuqi Jia, Runpeng Geng +2
A prompt injection attack aims to inject malicious instruction/data into the input of an LLM-Integrated Application such that it produces results as an attacker desires. Existing w…
MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use
Yue Huang, Jiawen Shi, Yuan Li +8
Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the…
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks
Kaijie Zhu, Jiaao Chen, Jindong Wang +3
Large language models (LLMs) have achieved remarkable performance in various evaluation benchmarks. However, concerns are raised about potential data contamination in their conside…