21 papers
Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching
Zhuoyi Peng, Hanlin Gu, Lixin Fan +1
Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce. Few-shot graph learning on TAGs is hard: with only a handful…
GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs
Zhuoyi Peng, Jingzhou Jiang, Hanlin Gu +2
Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and…
Quantifying and Defending against the Privacy Risk in Logit-based Federated Learning
Sheng Wan, Dashan Gao, Hanlin Gu +3
Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among clients. Unlike traditional parameter-based FL methods that e…
Federated Co-tuning Framework for Large and Small Language Models
Tao Fan, Yan Kang, Guoqiang Ma +4
By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a ga…
FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion
Tao Fan, Guoqiang Ma, Yuanfeng Song +3
Federated fine-tuning of Large Language Models (LLMs) is obstructed by a trilemma of challenges: protecting LLMs intellectual property (IP), ensuring client privacy, and mitigating…
Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without Disclosure
Hanlin Gu, Hong Xi Tae, Lixin Fan +1
This paper addresses the critical challenge of unlearning in Vertical Federated Learning (VFL), a setting that has received far less attention than its horizontal counterpart. Spec…