arXiv subjects

Chuan Chen

Publications and source records attributed to Chuan Chen.

At least 55 records · Page 3Linked to original sources

arXiv2023

VeryFL: A Verify Federated Learning Framework Embedded with Blockchain

Blockchain-empowered federated learning (FL) has provoked extensive research recently. Various blockchain-based federated learning algorithm, architecture and mechanism have been designed to solve issues like single point failure and data falsification brought by centralized FL paradigm. Moreover, it is easier to allocate incentives to nodes with the help of the blockchain. Various centralized federated learning frameworks like FedML, have emerged in the community to help boost the research on FL. However, decentralized blockchain-based federated learning framework is still missing, which cause inconvenience for researcher to reproduce or verify the algorithm performance based on blockchain. Inspired by the above issues, we have designed and developed a blockchain-based federated learning framework by embedding Ethereum network. This report will present the overall structure of this framework, which proposes a code practice paradigm for the combination of FL with blockchain and, at the same time, compatible with normal FL training task. In addition to implement some blockchain federated learning algorithms on smart contract to help execute a FL training, we also propose a model ownership authentication architecture based on blockchain and model watermarking to protect the intellectual property rights of models. These mechanism on blockchain shows an underlying support of blockchain for federated learning to provide a verifiable training, aggregation and incentive distribution procedure and thus we named this framework VeryFL (A Verify Federated Learninig Framework Embedded with Blockchain). The source code is avaliable on https://github.com/GTMLLab/VeryFL.

cs.LG↗

arXiv2023

Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platform

With the development of practical deep learning models like generative AI, their excellent performance has brought huge economic value. For instance, ChatGPT has attracted more than 100 million users in three months. Since the model training requires a lot of data and computing power, a well-performing deep learning model is behind a huge effort and cost. Facing various model attacks, unauthorized use and abuse from the network that threaten the interests of model owners, in addition to considering legal and other administrative measures, it is equally important to protect the model's copyright from the technical means. By using the model watermarking technology, we point out the possibility of building a unified platform for model ownership verification. Given the application history of blockchain in copyright verification and the drawbacks of a centralized third-party, this paper considers combining model watermarking technology and blockchain to build a unified model copyright protection platform. By a new solution we called Tokenized Model, it protects the model's copyright by reliable ownership record and verification mechanism. It also promotes the financial value of model by constructing the model's transaction process and contribution shares of a model. In the typical case study, we also study the various performance under usual scenario to verify the effectiveness of this platform.

cs.CR↗

arXiv2023

Hourglass-Like Spin Excitation in a Doped Mott Insulator

We examine the dynamical magnetic response in a two-component resonating-valence-bond (RVB) description of the doped Mott insulator. The half-filled antiferromagnetic phase described by the Schwinger-boson mean-field theory will evolve into a bosonic-RVB state in the superconducting phase upon doping, where the doped holes introduce another fermionic itinerant spinon which forms a BCS-like RVB order. The spin excitations are thus composed of a resonance-like mode from the former and a weak dispersive mode from the itinerant component at the mean-field level. These two-component spinons are shown to give rise to an hourglass-like spin excitation at the RPA level via an antiferromagnetic coupling between the two modes, which provides an unconventional explanation of the experimental observations in the cuprate. In particular, we also discuss an instability towards an incommensurate magnetic order in this theoretical framework.

cond-mat.str-el↗

arXiv2023

Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction

Avatars, as promising digital assistants in Vehicular Metaverses, can enable drivers and passengers to immerse in 3D virtual spaces, serving as a practical emerging example of Artificial Intelligence of Things (AIoT) in intelligent vehicular environments. The immersive experience is achieved through seamless human-avatar interaction, e.g., augmented reality navigation, which requires intensive resources that are inefficient and impractical to process on intelligent vehicles locally. Fortunately, offloading avatar tasks to RoadSide Units (RSUs) or cloud servers for remote execution can effectively reduce resource consumption. However, the high mobility of vehicles, the dynamic workload of RSUs, and the heterogeneity of RSUs pose novel challenges to making avatar migration decisions. To address these challenges, in this paper, we propose a dynamic migration framework for avatar tasks based on real-time trajectory prediction and Multi-Agent Deep Reinforcement Learning (MADRL). Specifically, we propose a model to predict the future trajectories of intelligent vehicles based on their historical data, indicating the future workloads of RSUs.Based on the expected workloads of RSUs, we formulate the avatar task migration problem as a long-term mixed integer programming problem. To tackle this problem efficiently, the problem is transformed into a Partially Observable Markov Decision Process (POMDP) and solved by multiple DRL agents with hybrid continuous and discrete actions in decentralized. Numerical results demonstrate that our proposed algorithm can effectively reduce the latency of executing avatar tasks by around 25% without prediction and 30% with prediction and enhance user immersive experiences in the AIoT-enabled Vehicular Metaverse (AeVeM).

cs.AI↗

arXiv2023

DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion

To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples. Among those, several works have achieved more advanced results via exploiting the structure information on KGs with Graph Convolutional Networks (GCN). However, we observe that entity embeddings aggregated from neighbors in different directions are just simply averaged to complete single-tasks by existing GCN based models, ignoring the specific requirements of forward and backward sub-tasks. In this paper, we propose a Direction-sensitive Multi-task GCN (DsMtGCN) to make full use of the direction information, the multi-head self-attention is applied to specifically combine embeddings in different directions based on various entities and sub-tasks, the geometric constraints are imposed to adjust the distribution of embeddings, and the traditional binary cross-entropy loss is modified to reflect the triple uncertainty. Moreover, the competitive experiments results on several benchmark datasets verify the effectiveness of our model.

cs.AI↗

arXiv2023

Contextual Dictionary Lookup for Knowledge Graph Completion

Knowledge graph completion (KGC) aims to solve the incompleteness of knowledge graphs (KGs) by predicting missing links from known triples, numbers of knowledge graph embedding (KGE) models have been proposed to perform KGC by learning embeddings. Nevertheless, most existing embedding models map each relation into a unique vector, overlooking the specific fine-grained semantics of them under different entities. Additionally, the few available fine-grained semantic models rely on clustering algorithms, resulting in limited performance and applicability due to the cumbersome two-stage training process. In this paper, we present a novel method utilizing contextual dictionary lookup, enabling conventional embedding models to learn fine-grained semantics of relations in an end-to-end manner. More specifically, we represent each relation using a dictionary that contains multiple latent semantics. The composition of a given entity and the dictionary's central semantics serves as the context for generating a lookup, thus determining the fine-grained semantics of the relation adaptively. The proposed loss function optimizes both the central and fine-grained semantics simultaneously to ensure their semantic consistency. Besides, we introduce two metrics to assess the validity and accuracy of the dictionary lookup operation. We extend several KGE models with the method, resulting in substantial performance improvements on widely-used benchmark datasets.

cs.AI↗

arXiv2023

Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond

Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI development. The content generated by related applications, such as text, images and audio, has sparked a heated discussion. Various derived AIGC applications are also gradually entering all walks of life, bringing unimaginable impact to people's daily lives. However, the rapid development of such generative tools has also raised concerns about privacy and security issues, and even copyright issues in AIGC. We note that advanced technologies such as blockchain and privacy computing can be combined with AIGC tools, but no work has yet been done to investigate their relevance and prospect in a systematic and detailed way. Therefore it is necessary to investigate how they can be used to protect the privacy and security of data in AIGC by fully exploring the aforementioned technologies. In this paper, we first systematically review the concept, classification and underlying technologies of AIGC. Then, we discuss the privacy and security challenges faced by AIGC from multiple perspectives and purposefully list the countermeasures that currently exist. We hope our survey will help researchers and industry to build a more secure and robust AIGC system.

cs.CR↗

arXiv2023

Capturing Fine-grained Semantics in Contrastive Graph Representation Learning

Graph contrastive learning defines a contrastive task to pull similar instances close and push dissimilar instances away. It learns discriminative node embeddings without supervised labels, which has aroused increasing attention in the past few years. Nevertheless, existing methods of graph contrastive learning ignore the differences between diverse semantics existed in graphs, which learn coarse-grained node embeddings and lead to sub-optimal performances on downstream tasks. To bridge this gap, we propose a novel Fine-grained Semantics enhanced Graph Contrastive Learning (FSGCL) in this paper. Concretely, FSGCL first introduces a motif-based graph construction, which employs graph motifs to extract diverse semantics existed in graphs from the perspective of input data. Then, the semantic-level contrastive task is explored to further enhance the utilization of fine-grained semantics from the perspective of model training. Experiments on five real-world datasets demonstrate the superiority of our proposed FSGCL over state-of-the-art methods. To make the results reproducible, we will make our codes public on GitHub after this paper is accepted.

cs.LG↗

arXiv2023

Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph

Anomaly detection on attributed graphs is a crucial topic for its practical application. Existing methods suffer from semantic mixture and imbalance issue because they mainly focus on anomaly discrimination, ignoring representation learning. It conflicts with the assortativity assumption that anomalous nodes commonly connect with normal nodes directly. Additionally, there are far fewer anomalous nodes than normal nodes, indicating a long-tailed data distribution. To address these challenges, a unique algorithm,Decoupled Self-supervised Learning forAnomalyDetection (DSLAD), is proposed in this paper. DSLAD is a self-supervised method with anomaly discrimination and representation learning decoupled for anomaly detection. DSLAD employs bilinear pooling and masked autoencoder as the anomaly discriminators. By decoupling anomaly discrimination and representation learning, a balanced feature space is constructed, in which nodes are more semantically discriminative, as well as imbalance issue can be resolved. Experiments conducted on various six benchmark datasets reveal the effectiveness of DSLAD.

cs.LG↗

arXiv2023

Nature of visons in the perturbed ferromagnetic and antiferromagnetic Kitaev honeycomb models

The Kitaev honeycomb model hosts a fascinating fractionalized state of matter featuring emergent Majorana fermions and a vison particle that carries the flux of an emergent gauge field. In the exactly solvable model these visons are static but certain perturbations can induce their motion. We show that the nature of the vison motion induced by a Zeeman field is sharply distinct in the ferromagnetic vs the antiferromagnetic Kitaev models. Namely, in the ferromagnetic model the vison has a trivial non-projective translational symmetry, whereas in the antiferromagnetic Kitaev model it has a projective translational symmetry with $π$-flux per unit cell. The vison band of the ferromagnetic case has zero Berry curvature, and no associated intrinsic contribution to the thermal Hall effect. In contrast, in the antiferromagnetic case there are two gapped vison bands with opposite Chern numbers and an associated intrinsic vison contribution to the thermal Hall effect. We discuss these findings in the light of the physics of the spin liquid candidate $α$-RuCl$_3$.

cond-mat.str-el↗

arXiv2023

A Dual-Purpose Deep Learning Model for Auscultated Lung and Tracheal Sound Analysis Based on Mixed Set Training

Many deep learning-based computerized respiratory sound analysis methods have previously been developed. However, these studies focus on either lung sound only or tracheal sound only. The effectiveness of using a lung sound analysis algorithm on tracheal sound and vice versa has never been investigated. Furthermore, no one knows whether using lung and tracheal sounds together in training a respiratory sound analysis model is beneficial. In this study, we first constructed a tracheal sound database, HF_Tracheal_V1, containing 10448 15-s tracheal sound recordings, 21741 inhalation labels, 15858 exhalation labels, and 6414 continuous adventitious sound (CAS) labels. HF_Tracheal_V1 and our previously built lung sound database, HF_Lung_V2, were either combined (mixed set), used one after the other (domain adaptation), or used alone to train convolutional neural network bidirectional gate recurrent unit models for inhalation, exhalation, and CAS detection in lung and tracheal sounds. The results revealed that the models trained using lung sound alone performed poorly in tracheal sound analysis and vice versa. However, mixed set training or domain adaptation improved the performance for 1) inhalation and exhalation detection in lung sounds and 2) inhalation, exhalation, and CAS detection in tracheal sounds compared to positive controls (the models trained using lung sound alone and used in lung sound analysis and vice versa). In particular, the model trained on the mixed set had great flexibility to serve two purposes, lung and tracheal sound analyses, at the same time.

cs.SD↗

arXiv2022

When Digital Economy Meets Web3.0: Applications and Challenges

With the continuous development of web technology, Web3.0 has attracted a considerable amount of attention due to its unique decentralized characteristics. The digital economy is an important driver of high-quality economic development and is currently in a rapid development stage. In the digital economy scenario, the centralized nature of the Internet and other characteristics usually bring about security issues such as infringement and privacy leakage. Therefore, it is necessary to investigate how to use Web3.0 technologies to solve the pain points encountered in the development of the digital economy by fully exploring the critical technologies of digital economy and Web3.0. In this paper, we discuss the aspects of Web3.0 that should be integrated with the digital economy to better find the entry point to solve the problems by examining the latest advances of Web3.0 in machine learning, finance, and data management. We hope this research will inspire those who are involved in both academia and industry, and finally help to build a favourable ecology for the digital economy.

cs.CY↗

arXiv2022

Emergent Topological Superconductor by Charge Density Wave Transition

Many-body instabilities and topological physics are two attractive topics in condensed matter physics. It is intriguing to explore the interplay between these phenomena in a single quantum material. Here, using the prototypical charge density wave (CDW) material monolayer 1H-NbSe$_2$ as an example, we show how momentum-dependent electron-phonon coupling drives the CDW transition from $3\times3$ to $2\times2$ phase under electron doping. More interestingly, we find the coexistence of superconductivity and nontrivial topology in one of the two $2\times2$ CDW phases, the latter of which is identified by the nonzero Z$_2$ invariant with ideal Dirac cone edge states near the Fermi level. A similar CDW transition-induced topological superconductor has also been confirmed in monolayer 1H-TaSe$_2$. Our findings not only reveal a unique and general method to introduce nontrivial topology by CDW transition, but also provide an ideal platform to modulate different quantum orders by electron doping, thus stimulating experimental interest.

cond-mat.supr-con↗

arXiv2022

Berry Phases of Vison Transport in $\mathbb{Z}_2$ Topologically Ordered States from Exact Fermion-Flux Lattice Dualities

We develop an exact map of all states and operators from 2D lattices of spins-$1/2$ into lattices of fermions and bosons with mutual semionic statistical interaction that goes beyond previous dualities of $\mathbb{Z}_2$ lattice gauge theories because it does not rely on imposing local conservation laws and captures the motion of ``charges'' and ``fluxes'' on equal footing. This map allows to explicitly compute the Berry phases for the transport of fluxes in a large class of symmetry enriched topologically ordered states with emergent $\mathbb{Z}_2$ gauge fields that includes chiral, non-chiral, abelian or non-abelian, that can be perturbatively connected to models where the visons are static and the emergent fermionic spinons have a non-interacting dispersion. The numerical complexity of computing such vison phases reduces therefore to computing overlaps of ground states of free-fermion Hamiltonians. Among other results, we establish numerically the conditions under which the Majorana-carrying flux excitation in Ising-Topologically-Ordered states enriched by translations acquires $0$ or $π$ phase when moving around a single plaquette.

cond-mat.str-el↗

arXiv2022

FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs

As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, in some practical scenarios, graph data are stored separately in multiple distributed parties, which may not be directly shared due to conflicts of interest. Hence, federated graph neural networks are proposed to address such data silo problems while preserving the privacy of each party (or client). Nevertheless, different graph data distributions among various parties, which is known as the statistical heterogeneity, may degrade the performance of naive federated learning algorithms like FedAvg. In this paper, we propose FedEgo, a federated graph learning framework based on ego-graphs to tackle the challenges above, where each client will train their local models while also contributing to the training of a global model. FedEgo applies GraphSAGE over ego-graphs to make full use of the structure information and utilizes Mixup for privacy concerns. To deal with the statistical heterogeneity, we integrate personalization into learning and propose an adaptive mixing coefficient strategy that enables clients to achieve their optimal personalization. Extensive experimental results and in-depth analysis demonstrate the effectiveness of FedEgo.

cs.LG↗

arXiv2022

A Monotonous Intuitionistic Fuzzy TOPSIS Method under General Linear Orders via Admissible Distance Measures

All intuitionistic fuzzy TOPSIS methods contain two key elements: (1) the order structure, which can affect the choices of positive ideal-points and negative ideal-points, and construction of admissible distance/similarity measures; (2) the distance/similarity measure, which is closely related to the values of the relative closeness degrees and determines the accuracy and rationality of decision-making. For the order structure, many efforts are devoted to constructing some score functions, which can strictly distinguish different intuitionistic fuzzy values (IFVs) and preserve the natural partial order for IFVs.This paper proves that such a score function does not exist, namely the application of a single monotonous and continuous function does not distinguish all IFVs. For the distance or similarity measure, some examples are given to show that classical similarity measures based on the normalized Euclidean distance and normalized Minkowski distance do not meet the axiomatic definition of intuitionistic fuzzy similarity measures. Moreover,some illustrative examples are given to show that classical intuitionistic fuzzy TOPSIS methods do not ensure the monotonicity with the natural partial order or linear orders, which may yield some counter-intuitive results. To overcome the limitation of non-monotonicity, we propose a novel intuitionistic fuzzy TOPSIS method,using three new admissible distances with the linear orders measured by a score degree/similarity function and accuracy degree, or two aggregation functions, and prove that the proposed TOPSIS method is monotonous under these three linear orders.} This is the first result with a strict mathematical proof on the monotonicity with the linear orders for the intuitionistic fuzzy TOPSIS method.

math.GM↗

arXiv2022

A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee

Federated learning allows multiple participants to collaboratively train an efficient model without exposing data privacy. However, this distributed machine learning training method is prone to attacks from Byzantine clients, which interfere with the training of the global model by modifying the model or uploading the false gradient. In this paper, we propose a novel serverless federated learning framework Committee Mechanism based Federated Learning (CMFL), which can ensure the robustness of the algorithm with convergence guarantee. In CMFL, a committee system is set up to screen the uploaded local gradients. The committee system selects the local gradients rated by the elected members for the aggregation procedure through the selection strategy, and replaces the committee member through the election strategy. Based on the different considerations of model performance and defense, two opposite selection strategies are designed for the sake of both accuracy and robustness. Extensive experiments illustrate that CMFL achieves faster convergence and better accuracy than the typical Federated Learning, in the meanwhile obtaining better robustness than the traditional Byzantine-tolerant algorithms, in the manner of a decentralized approach. In addition, we theoretically analyze and prove the convergence of CMFL under different election and selection strategies, which coincides with the experimental results.

cs.LG↗

arXiv2022

Decisive role of electron-phonon coupling for phonon and electron instabilities in transition metal dichalcogenides

The origin of the charge density wave (CDW) in transition metal dichalcognides has been in hot debate and no conclusive agreement has been reached. Here, we propose an ab-initio framework for an accurate description of both Fermi surface nesting and electron-phonon coupling (EPC) and systematically investigate their roles in the formation of CDW. Using monolayer 1H-NbSe$_2$ and 1T-VTe$_2$ as representative examples, we show that it is the momentum-dependent EPC softens the phonon frequencies, which become imaginary (phonon instabilities) at CDW vectors (indicating CDW formation). Besides, the distribution of the CDW gap opening (electron instabilities) can be correctly predicted only if EPC is included in the mean-field model. These results emphasize the decisive role of EPC in the CDW formation. Our analytical process is general and can be applied to other CDW systems.

cond-mat.mtrl-sci↗