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Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes

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arXiv 2023年

作者： Guo, Shunxin Wang, Hongsong Geng, Xin MOE Key Laboratory of Computer Network and Information Integration China School of Computer Science and Engineering Southeast University Nanjing210096 China

Federated learning shows promise as a privacy-preserving collaborative learning technique. Existing heterogeneous federated learning mainly focuses on skewing the label distribution across clients. However, most approaches suffer from catastrophic forgetting and concept drift, mainly when the global distribution of all classes is extremely unbalanced and the data distribution of the client dynamically evolves over time. In this paper, we study the new task, i.e., Dynamic Heterogeneous Federated Learning (DHFL), which addresses the practical scenario where heterogeneous data distributions exist among different clients and dynamic tasks within the client. Accordingly, we propose a novel federated learning framework named Federated Multi-Level Prototypes (FedMLP) and design federated multi-level regularizations. To mitigate concept drift, we construct prototypes and semantic prototypes to provide fruitful generalization knowledge and ensure the continuity of prototype spaces. To maintain the model stability and consistency of convergence, three regularizations are introduced as training losses, i.e., prototype-based regularization, semantic prototype-based regularization, and federated inter-task regularization. Extensive experiments show that the proposed method achieves state-of-the-art performance in various settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

KGAPG: Knowledge-Aware Neural Group Representation Learning for Attentive Prescription Generation of Traditional Chinese Medicine

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KGAPG: Knowledge-Aware Neural Group Representation Learning ...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Li, Shuchen Wang, Wei He, Jieyue MOE Southeast University Key Lab of Computer Network Information Integration Nanjing China

ISBN: (纸本)9781665401265

Prescriptions play an essential role in the process of Traditional Chinese Medicine (TCM) diagnosis and treatment. Prescription generation is to generate a set of herbs to treat the symptoms of a patient by analyzing the relationship between symptoms and herbs. Although there have been a couple of studies to generate prescriptions, they have ignored the implicit relationship between the different symptoms of the patients. In addition, abundant semantic information and interpretability of the knowledge graph can help to portray the complicated relationships between the various modules of TCM. Therefore, this paper proposes a Knowledge-Aware neural Group representation learning model for Attentive Prescription Generation of Traditional Chinese Medicine (KGAPG), which regards the prescription generation task as a group recommendation problem. More specifically, multiple symptoms of a patient are considered as a symptom group and the complicated semantic information between symptoms and herbs can be captured by the knowledge graph. The syndrome information of multiple symptoms which is summarized by a group aggregation method based on the attention mechanism is applied to simulate the actual process of TCM diagnosis and treatment. The experiment results demonstrate that KGAPG is effective on a TCM prescription benchmark dataset, and its evaluation indicators of Precision, Recall and NDCG exceed other state-of-the-art methods. © 2021 IEEE.

关键词： Knowledge graph

Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system network science Overexploitation Regime shift Metastability

PE: A Poincare Explanation Method for Fast Text Hierarchy Generation

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PE: A Poincare Explanation Method for Fast Text Hierarchy Ge...

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chen, Qian Li, Dongyang He, Xiaofeng Li, Hongzhao Yi, Hongyu School of Computer Science and Technology East China Normal University Shanghai China NPPA Key Laboratory of Publishing Integration Development ECNUP Shanghai China Sichuan Caizi Software Information Network Co. Ltd. China

ISBN: (纸本)9798891761681

The black-box nature of deep learning models in NLP hinders their widespread *** research focus has shifted to Hierarchical Attribution (HA) for its ability to model feature *** works model non-contiguous combinations with a time-costly greedy search in Euclidean spaces, neglecting underlying linguistic information in feature *** this work, we introduce a novel method, namely Poincare Explanation (PE), for modeling feature interactions with hyperbolic spaces in a time efficient ***, we take building text hierarchies as finding spanning trees in hyperbolic *** we project the embeddings into hyperbolic spaces to elicit inherit semantic and syntax hierarchical *** we propose a simple yet effective strategy to calculate Shapley *** we build the the hierarchy with proving the constructing process in the projected space could be viewed as building a minimum spanning tree and introduce a time efficient building *** results demonstrate the effectiveness of our approach. © 2024 Association for Computational Linguistics.

关键词： Semantics

SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding

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arXiv 2024年

作者： Wang, Zhenglin Wu, Jialong Lai, Yilong Zhang, Congzhi Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Large Language Models (LLMs) demonstrate remarkable emergent abilities across various tasks, yet fall short of complex reasoning and planning tasks. The tree-search-based reasoning methods address this by encouraging the exploration of intermediate steps, surpassing the capabilities of chain-of-thought prompting. However, significant inference latency is introduced due to the systematic exploration and evaluation of multiple thought paths. This paper introduces SEED, a novel and efficient inference framework to improve both runtime speed and GPU memory management concurrently. Based on a scheduled speculative execution, SEED efficiently handles multiple iterations for thought generation and state evaluation, leveraging a rounds-scheduled strategy to manage draft model dispatching. Extensive experimental evaluations on three reasoning datasets demonstrate the superior speedup performance of SEED1 Copyright © 2024, The Authors. All rights reserved.

关键词： Memory management

Finformer: A Static-dynamic Spatiotemporal Framework for Stock Trend Prediction

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Finformer: A Static-dynamic Spatiotemporal Framework for Sto...

2023 IEEE International Conference on Big Data, BigData 2023

作者： Zu, Yi Mi, Jiacong Song, Lingning Lu, Shan He, Jieyue Southeast University School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Nanjing China Southeast University School of Software Engineering Nanjing China Nanjing Fenghuo Tiandi Communication Technology Co. Ltd Nanjing China

ISBN: (纸本)9798350324457

The core of quantitative investment lies in predicting future trends in stock prices. The future trend of a stock is closely related to the industry it belongs to and its relationship with other stocks. Although some research has focused on stock trend prediction in recent years, most studies have only considered the stock's own time series feature, neglecting the spatial features between stocks. Some research has incorporated spatial information, but typically only considered predefined static relationships. At the same time, capturing dynamic spatial information in the market has been a long-standing challenge. Thus, we propose a spatio-temporal model, Finformer, in order to go beyond traditional time series models. We designed a sparse static-dynamic transformer to capture dynamic market spatial information as it changes over time and combined predefined relationships to extract highly correlated spatial features in the stock market. To effectively integrate spatial and temporal features, we introduced an adaptive spatio-temporal fusion module that dynamically fuses spatio-temporal features based on market conditions at different periods. Experiments on two real-world stock market datasets show that our proposed model outperforms the state-of-the-art baselines in the signal-based and portfolio-based metrics, which are widely concerned in the financial field. Ablation study and hyper-parameter study further reveal the effectiveness of each module in the model and the impact of hyper-parameters. The code will be made publicly available. 1 © 2023 IEEE.

关键词： computational finance deep learning stock trend prediction transformer

Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment

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arXiv 2024年

作者： Zhang, Congzhi Zhang, Linhai Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Multi-hop fact verification aims to detect the veracity of the given claim by integrating and reasoning over multiple pieces of evidence. Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets. Among the various debiasing works, the causal inference-based methods become popular by performing theoretically guaranteed debiasing such as casual intervention or counterfactual reasoning. However, existing causal inference-based debiasing methods, which mainly formulate fact verification as a single-hop reasoning task to tackle shallow bias patterns, cannot deal with the complicated bias patterns hidden in multiple hops of evidence. To address the challenge, we propose Causal Walk, a novel method for debiasing multi-hop fact verification from a causal perspective with front-door adjustment. Specifically, in the structural causal model, the reasoning path between the treatment (the input claim-evidence graph) and the outcome (the veracity label) is introduced as the mediator to block the confounder. With the front-door adjustment, the causal effect between the treatment and the outcome is decomposed into the causal effect between the treatment and the mediator, which is estimated by applying the idea of random walk, and the causal effect between the mediator and the outcome, which is estimated with normalized weighted geometric mean approximation. To investigate the effectiveness of the proposed method, an adversarial multi-hop fact verification dataset and a symmetric multi-hop fact verification dataset are proposed with the help of the large language model. Experimental results show that Causal Walk outperforms some previous debiasing methods on both existing datasets and the newly constructed datasets. Code and data will be released at https://***/zcccccz/CausalWalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

ADACQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment

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arXiv 2024年

作者： Lai, Yilong Wu, Jialong Zhang, Congzhi Sun, Haowen Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Conversational Query Reformulation (CQR) has significantly advanced in addressing the challenges of conversational search, particularly those stemming from the latent user intent and the need for historical context. Recent works aimed to boost the performance of CQR through alignment. However, they are designed for one specific retrieval system, which potentially results in sub-optimal generalization. To overcome this limitation, we present a novel framework ADACQR. By aligning reformulation models with both term-based and semantic-based retrieval systems, ADACQR enhances the generalizability of information-seeking queries among diverse retrieval environments through a two-stage training strategy. Moreover, two effective approaches are proposed to obtain superior labels and diverse input candidates, boosting the efficiency and robustness of the framework. Experimental results on the TopiOCQA, QReCC and TREC CAsT datasets demonstrate that ADACQR outperforms the existing methods in a more efficient framework, offering both quantitative and qualitative improvements in conversational query reformulation.1 Copyright © 2024, The Authors. All rights reserved.

关键词： Structured Query Language

Brainnetformer: Decoding Brain Cognitive States with Spatial-Temporal Cross Attention 48

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Brainnetformer: Decoding Brain Cognitive States with Spatial...

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sheng, Leheng Wang, Wenhan Shi, Zhiyi Zhan, Jichao Kong, Youyong Southeast University Chien-Shiung Wu College China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China

ISBN: (纸本)9781728163277

Learning about the cognitive state of the brain has always been a popular topic. Based on the fact that fluctuations of brain signals and functional connectome (FC) relate to specific human behaviors, deep learning based methods have shown promising results on the prediction of such behaviors by analyzing biological signals. Existing methods either model from static perspectives or apply spatial-temporal graph convolution to extract dynamic properties. However, the static information and dynamic information can reflect global brain activities and local brain activities respectively. Thus, we propose BrainNetFormer to incorporate both static and dynamic properties for human behavior prediction. To be specific, a spatial cross attention module and a temporal cross attention module are introduced for information fusion. In addition, since a specific behavior of subjects can be decomposed into a series of subtasks, we introduce a sub-task regularization loss to assist in training and empower the model to recognize subtasks at each moment. Experiments on the HCP-Task dataset demonstrate the superior performance of the proposed model. © 2023 IEEE.

关键词： brain states decoding cross attention functional MRI images Spatial-temporal graph convolution