10 citations · 54 across the 36 of their papers we have counts for
37 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…
Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
Junxiang Wu, Zhiqiang Kou, Hongwei Zeng +7
Label Distribution Learning (LDL) models supervision as an instance-wise probability distribution, enabling fine-grained learning under inherent ambiguity, but its success relies o…
FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching
He Yang, Dongyi Lv, Wei Xi +3
Most existing Byzantine-robust federated learning (FL) methods suffer from slow and unstable convergence. Moreover, when handling a substantial proportion of colluded malicious cli…
InkDrop: Invisible Backdoor Attacks Against Dataset Condensation
He Yang, Dongyi Lv, Song Ma +4
Dataset Condensation (DC) is a data-efficient learning paradigm that synthesizes small yet informative datasets, enabling models to match the performance of full-data training. How…
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…
ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Xuemei Cao, Hanlin Gu, Xin Yang +4
Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, w…
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…
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…
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…
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…
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…
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
Gongxi Zhu, Donghao Li, Hanlin Gu +3
Federated Learning (FL) is a promising approach for training machine learning models on decentralized data while preserving privacy. However, privacy risks, particularly Membership…
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
Hanlin Gu, Xinyuan Zhao, Gongxi Zhu +4
Federated learning (FL) enables multiple clients to collaboratively learn a shared model without sharing their individual data. Concerns about utility, privacy, and training effici…
Grounding Foundation Models through Federated Transfer Learning: A General Framework
Yan Kang, Tao Fan, Hanlin Gu +3
Foundation Models (FMs) such as GPT-4 encoded with vast knowledge and powerful emergent abilities have achieved remarkable success in various natural language processing and comput…
A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models
Yuanfeng Song, Yuanqin He, Xuefang Zhao +5
The springing up of Large Language Models (LLMs) has shifted the community from single-task-orientated natural language processing (NLP) research to a holistic end-to-end multi-tas…