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16th Asian Conference on Machine Learning, ACML 2024

作者： Xie, Zaipeng Li, Likun Chen, Xiangbin Yu, Hao Huang, Qian Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China College of Artificial Intelligence and Automation Hohai University Nanjing China

Federated graph learning enhances federated learning by enabling privacy-preserving collaborative training on distributed graph data. While traditional methods are effective in managing data heterogeneity, they typically assume static graph structures, overlooking the dynamic nature of real-world graphs. Integrating federated graph learning with dynamic graph neural networks addresses this issue but often fails to retain previously acquired knowledge, limiting generalization for both global and personalized models. This paper proposes FedDGL, a novel framework designed to address temporal evolution, and data heterogeneity in federated dynamic graph learning. Unlike conventional approaches, FedDGL captures temporal dynamics through a global knowledge distillation technique and manages client heterogeneity using a global prototype-based regularization method. The framework employs contrastive learning to generate global prototypes, enhancing feature representation while utilizing a prototype similarity-based personalized aggregation strategy for effective adaptation to local and global data distributions. Experiments on multiple benchmark datasets show that FedDGL achieves significant performance improvements over state-of-the-art methods, with up to 9.02% and 8.77% gains in local and global testing, respectively, compared to FedAvg. These results highlight FedDGL’s effectiveness in improving personalized and global model performance in dynamic, heterogeneous federated graph learning scenarios. © 2024 Z. Xie, L. Li, X. Chen, H. Yu & Q. Huang.

关键词： Dynamic Graph Learning Federated Graph Learning Knowledge Distillation Personalized Aggregation Prototype Learning Temporal Evolution

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Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

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computers, Materials & Continua 2024年第3期78卷 3177-3194页

作者： Bin Wu Xianyi Chen Jinzhou Huang Caicai Zhang Jing Wang Jing Yu Zhiqiang Zhao Zhuolin Mei School of Computer and Big Data Science Jiujiang UniversityJiujiang332005China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Engineering Hubei University of Arts and ScienceXiangyang441053China School of Modern Information Technology Zhejiang Institute of Mechanical and Electrical EngineeringHangzhou310053China Information Center Jiangxi Changjiang Chemical Co.Ltd.Jiujiang332005China School of Mathematics and Computer Science Ningxia Normal UniversityGuyuan756099China

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so *** owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud *** on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure *** issue of privacy data protection has become an important obstacle to data sharing and *** to query outsourcing graph data safely and effectively has become the focus of *** query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same *** work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a *** our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud *** proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental *** research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

关键词： Privacy-preserving adjacency query multi-keyword fuzzy search encrypted graph

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PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECASTING 12

PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Dai, Tao Wu, Beiliang Liu, Peiyuan Li, Naiqi Bao, Jigang Jiang, Yong Xia, Shu-Tao College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China WeBank Institute of Financial Technology Shenzhen University China

Convolutional neural network (CNN)-based and Transformer-based methods have recently made significant strides in time series forecasting, which excel at modeling local temporal variations or capturing long-term dependencies. However, real-world time series usually contain intricate temporal patterns, thus making it challenging for existing methods that mainly focus on temporal variations modeling from the 1D time series directly. Based on the intrinsic periodicity of time series, we propose a novel Periodicity Decoupling Framework (PDF) to capture 2D temporal variations of decoupled series for long-term series forecasting. Our PDF mainly consists of three components: multi-periodic decoupling block (MDB), dual variations modeling block (DVMB), and variations aggregation block (VAB). Unlike the previous methods that model 1D temporal variations, our PDF mainly models 2D temporal variations, decoupled from 1D time series by MDB. After that, DVMB attempts to further capture short-term and long-term variations, followed by VAB to make final predictions. Extensive experimental results across seven real-world long-term time series datasets demonstrate the superiority of our method over other state-of-the-art methods, in terms of both forecasting performance and computational efficiency. Code is available at https://***/Hank0626/PDF. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Time series

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MLRP-KG: Mine Landslide Risk Prediction Based on Knowledge Graph

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第1期3卷 78-87页

作者： Ma, Lianbo Wang, Jingwei Cheng, Jian Wang, Xingwei Zhu, Wancheng Northeastern University Shenyang China College of Software Northeastern University Shenyang China Research Institute of Mine Big Data China Coal Research Institute Beijing China College of Computer Science and Engineering Northeastern University Shenyang China School of Resource and Civil Engineering Northeastern University Shenyang China

The mine landslide risk prediction is a fundamental task for the safety management of the digital mining system, which is dependent on the analysis of the open pit mine exploitation slope stability. Such stability analysis involves a lot of local natural and human factors. Training a learning model using these factors, with the aim of discovering their relationship with the slope stability, is an intuitive way for the mine landslide risk prediction. The key issue is how to explore the complex nonlinear relations among these factors by using a small amount of high-dimensional historical slope data. Traditional factor-led methods for the issue only focus on the impacts of landslide factors, but ignore the correlations between historical slope data, which in fact contain more useful information. In this article, we propose a new mine landslide risk prediction model using knowledge graph. The gradient boosting decision tree is applied to further exploit the crossed features within the historical data, and then a landslide semantic network is constructed using knowledge graph with the consideration of correlation information between historical slope data. In this way, the model is suitable to deal with the small set of high-dimensional data, and it makes a joint use of the features within the landslide factor values and correlations between historical slope data. We conduct a set of experiments on real historical slope data from the real-world open pit mining scene. The experiment results validate the effectiveness and efficiency of the proposed model in predicting landslide risks with a small set of high-dimensional historical data. © 2020 IEEE.

关键词： Landslides

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features 13

Evolutionary Multiobjective Feature Selection Assisted by Un...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Duan, Xuan Liu, Songbai Ji, Junkai Li, Lingjie Lin, Qiuzhen Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350308365

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets. © 2024 IEEE.

关键词： Feature Selection

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SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification 24

SAM-MIL: A Spatial Contextual Aware Multiple Instance Learni...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Fang, Heng Huang, Sheng Tang, Wenhao Huangfu, Luwen Liu, Bo School of Big Data & Software Engineering Chongqing University Chongqing China Fowler College of Business San Diego State University San DiegoCA United States School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Multiple Instance Learning (MIL) represents the predominant framework in Whole Slide Image (WSI) classification, covering aspects such as sub-typing, diagnosis, and beyond. Current MIL models predominantly rely on instance-level features derived from pretrained models such as ResNet. These models segment each WSI into independent patches and extract features from these local patches, leading to a significant loss of global spatial context and restricting the model's focus to merely local features. To address this issue, we propose a novel MIL framework, named SAM-MIL, that emphasizes spatial contextual awareness and explicitly incorporates spatial context by extracting comprehensive, image-level information. The Segment Anything Model (SAM) represents a pioneering visual segmentation foundational model that can capture segmentation features without the need for additional fine-tuning, rendering it an outstanding tool for extracting spatial context directly from raw WSIs. Our approach includes the design of group feature extraction based on spatial context and a SAM-Guided Group Masking strategy to mitigate class imbalance issues. We implement a dynamic mask ratio for different segmentation categories and supplement these with representative group features of categories. Moreover, SAM-MIL divides instances to generate additional pseudo-bags, thereby augmenting the training set, and introduces consistency of spatial context across pseudo-bags to further enhance the model's performance. Experimental results on the CAMELYON-16 and TCGA Lung Cancer datasets demonstrate that our proposed SAM-MIL model outperforms existing mainstream methods in WSIs classification. Our open-source implementation code is is available at https://***/FangHeng/SAM-MIL. © 2024 ACM.

关键词： Self-supervised learning

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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FLEKE: Federated Locate-then-Edit Knowledge Editing

arXiv

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arXiv 2025年

作者： Zhao, Zongkai Xu, Guozeng Li, Xiuhua Wei, Kaiwen Zhong, Jiang School of Big Data & Software Engineering Chongqing University China College of Computer Science Chongqing University China

Locate-then-Edit Knowledge Editing (LEKE) is a key technique for updating large language models (LLMs) without full retraining. However, existing methods assume a single-user setting and become inefficient in real-world multi-client scenarios, where decentralized organizations (e.g., hospitals, financial institutions) independently update overlapping knowledge, leading to redundant mediator knowledge vector (MKV) computations and privacy concerns. To address these challenges, we introduce Federated Locate-then-Edit Knowledge Editing (FLEKE), a novel task that enables multiple clients to collaboratively perform LEKE while preserving privacy and reducing computational overhead. To achieve this, we propose FedEdit, a two-stage framework that optimizes MKV selection and reuse. In the first stage, clients locally apply LEKE and upload the computed MKVs. In the second stage, rather than relying solely on server-based MKV sharing, FLEKE allows clients retrieve relevant MKVs based on cosine similarity, enabling knowledge re-edit and minimizing redundant computations. Experimental results on two benchmark datasets demonstrate that FedEdit retains over 96% of the performance of non-federated LEKE while significantly outperforming a FedAvg-based baseline by approximately twofold. Besides, we find that MEMIT performs more consistently than PMET in the FLEKE task with our FedEdit framework. Our code is available at https://***/zongkaiz/FLEKE. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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RSP-gcForest: A Distributed Deep Forest via Random Sample Partition

RSP-gcForest: A Distributed Deep Forest via Random Sample Pa...

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2023 IEEE International Conference on big data, bigdata 2023

作者： Li, Mark Junjie Cai, Wenzhu Lin, Yigang Huang, Sunjie Huang, Joshua Zhexue Peng, Patrick Xiaogang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen Academy of Inspection and Quarantine China

ISBN: (纸本)9798350324457

Deep Forest, a powerful alternative to deep neural networks, has gained much attention due to its advantages, such as low complexity, minimal hyperparameter requirements, and strong application performance. In the current big data environment, where data volumes and model complexities are growing rapidly, distributed computing is needed to increase computational efficiency. Recently, a distributed deep forest approach, called BLB-gcForest (Bag of Little Bootstraps-gcForest), has been successful in reducing training instances within cascade forests, combining BLB and granularity segmentation, thus improving the computational efficiency and scalability of distributed deep forests. However, it still transmits the entire data set between layers and requires double sampling with BLB, limiting the amount of data and the scalability of resource utilization. This paper introduces a novel algorithm, RSP-gcForest, based on Random Sample Partition (RSP) to improve distributed deep forests computational efficiency and scalability. RSP-gcForest uses block-level samples that replace the full dataset, significantly reducing interlayer instance transmission and prediction within cascade forests. Additionally, RSP blocks are integrated with the segmentation granularity of cascade forests for ensemble learning, effectively addressing computational efficiency and resource constraints. We conducted experiments on four extensive datasets using Spark and evaluated performance across five key metrics. The results clearly show that RSP-gcForest, while maintaining high classification quality, surpasses state-of-the-art methods in terms of computational efficiency and resource utilization. Furthermore, it achieves superior load balancing, demonstrating its potential as a powerful tool in big data and distributed computing. © 2023 IEEE.

关键词： Deep forest Distributed Computing Ensemble Learning Random Sample Partition

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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