33 citations · 162 across the 52 of their papers we have counts for
54 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…
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…
FedGRPO: Privately Optimizing Foundation Models with Group-Relative Rewards from Domain Client
Gongxi Zhu, Hanlin Gu, Lixin Fan +2
One important direction of Federated Foundation Models (FedFMs) is leveraging data from small client models to enhance the performance of a large server-side foundation model. Exis…
Order-Level Attention Similarity Across Language Models: A Latent Commonality
Jinglin Liang, Jin Zhong, Shuangping Huang +5
In this paper, we explore an important yet previously neglected question: Do context aggregation patterns across Language Models (LMs) share commonalities? While some works have in…
Large-Small Model Collaborative Framework for Federated Continual Learning
Hao Yu, Xin Yang, Boyang Fan +4
Continual learning (CL) for Foundation Models (FMs) is an essential yet underexplored challenge, especially in Federated Continual Learning (FCL), where each client learns from a p…
Ten Challenging Problems in Federated Foundation Models
Tao Fan, Hanlin Gu, Xuemei Cao +30
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of fed…
Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
Zhaomin Wu, Zhen Qin, Junyi Hou +4
Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm that enables multiple parties with distinct feature sets to jointly train machine learning…
PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation
Tao Fan, Guoqiang Ma, Yuanfeng Song +2
Compressing Large Language Models (LLMs) into task-specific Small Language Models (SLMs) encounters two significant challenges: safeguarding domain-specific knowledge privacy and m…
Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
Hao Yu, Xin Yang, Le Zhang +4
Federated continual learning (FCL) allows each client to continually update its knowledge from task streams, enhancing the applicability of federated learning in real-world scenari…
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…
Disentangling data distribution for Federated Learning
Xinyuan Zhao, Hanlin Gu, Lixin Fan +2
Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data pr…
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…
Model-based Large Language Model Customization as Service
Zhaomin Wu, Jizhou Guo, Junyi Hou +3
Prominent Large Language Model (LLM) services from providers like OpenAI and Google excel at general tasks but often underperform on domain-specific applications. Current customiza…
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning
Jinglin Liang, Jin Zhong, Hanlin Gu +6
Federated Class Continual Learning (FCCL) merges the challenges of distributed client learning with the need for seamless adaptation to new classes without forgetting old ones. The…
FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
Hanlin Gu, Jiahuan Luo, Yan Kang +5
Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The conce…
FedCoT: Federated Chain-of-Thought Distillation for Large Language Models
Tao Fan, Weijing Chen, Yan Kang +5
Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks. However, their deploymen…
FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models
Tao Fan, Guoqiang Ma, Yan Kang +5
Recent research in federated large language models (LLMs) has primarily focused on enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on trans…
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data
Haoran Li, Xinyuan Zhao, Dadi Guo +6
As large language models (LLMs) demonstrate unparalleled performance and generalization ability, LLMs are widely used and integrated into various applications. When it comes to sen…
A Survey on Contribution Evaluation in Vertical Federated Learning
Yue Cui, Chung-ju Huang, Yuzhu Zhang +4
Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing. VFL fa…
No Free Lunch Theorem for Privacy-Preserving LLM Inference
Xiaojin Zhang, Yahao Pang, Yan Kang +4
Individuals and businesses have been significantly benefited by Large Language Models (LLMs) including PaLM, Gemini and ChatGPT in various ways. For example, LLMs enhance productiv…
Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
Hanlin Gu, Win Kent Ong, Chee Seng Chan +1
The advent of Federated Learning (FL) highlights the practical necessity for the right to be forgotten for all clients, allowing them to request data deletion from the machine lear…
Unlearning during Learning: An Efficient Federated Machine Unlearning Method
Hanlin Gu, Gongxi Zhu, Jie Zhang +4
In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the right to be forgotte…