Search Results for author: Han Xie

Found 10 papers, 3 papers with code

ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis

no code implementations14 Feb 2025 Xinpeng Wang, Rong Zhou, Han Xie, Xiaoying Tang, Lifang He, Carl Yang

Building on this realistic simulation, we propose ClusMFL, a novel MFL framework that leverages feature clustering for cross-institutional brain imaging analysis under modality incompleteness.

Contrastive Learning Federated Learning +1

AutoG: Towards automatic graph construction from tabular data

no code implementations25 Jan 2025 Zhikai Chen, Han Xie, Jian Zhang, Xiang Song, Jiliang Tang, Huzefa Rangwala, George Karypis

The absence of dedicated datasets to formalize and evaluate the effectiveness of graph construction methods, and 2.

graph construction

FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling

no code implementations22 Jan 2025 Emir Ceyani, Han Xie, Baturalp Buyukates, Carl Yang, Salman Avestimehr

Recently proposed personalized subgraph FL methods have become the de-facto standard for training personalized Graph Neural Networks (GNNs) in a federated manner while dealing with the missing links across clients' subgraphs due to privacy restrictions.

Federated Learning Privacy Preserving

GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning

no code implementations13 Jun 2024 Zhen Xiang, Linzhi Zheng, YanJie Li, Junyuan Hong, Qinbin Li, Han Xie, Jiawei Zhang, Zidi Xiong, Chulin Xie, Carl Yang, Dawn Song, Bo Li

We also show that GuardAgent is able to define novel functions in adaption to emergent LLM agents and guard requests, which underscores its strong generalization capabilities.

Semi-Supervised Domain Generalization with Evolving Intermediate Domain

1 code implementation19 Nov 2021 Luojun Lin, Han Xie, Zhishu Sun, WeiJie Chen, Wenxi Liu, Yuanlong Yu, Lei Zhang

From this perspective, we introduce a novel paradigm of DG, termed as Semi-Supervised Domain Generalization (SSDG), to explore how the labeled and unlabeled source domains can interact, and establish two settings, including the close-set and open-set SSDG.

Domain Generalization Semi-Supervised Domain Generalization

Federated Graph Classification over Non-IID Graphs

1 code implementation NeurIPS 2021 Han Xie, Jing Ma, Li Xiong, Carl Yang

Federated learning has emerged as an important paradigm for training machine learning models in different domains.

Clustering Dynamic Time Warping +4

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

1 code implementation14 Apr 2021 Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr

FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.

Federated Learning Graph Neural Network +1

Cannot find the paper you are looking for? You can Submit a new open access paper.