Search Results for author: Mingsong Chen

Found 30 papers, 4 papers with code

Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning

no code implementations15 Dec 2023 Xiao Du, Yutong Ye, Pengyu Zhang, Yaning Yang, Mingsong Chen, Ting Wang

To this end, in this paper, we propose a novel MARL algorithm named Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning (SCIC), which incorporates a novel Intrinsic reward mechanism based on a new cooperation criterion measured by situation-dependent causal influence among agents.

Multi-agent Reinforcement Learning reinforcement-learning

AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems

no code implementations23 Nov 2023 Ruixuan Liu, Ming Hu, Zeke Xia, Jun Xia, Pengyu Zhang, Yihao Huang, Yang Liu, Mingsong Chen

On the one hand, to achieve model training in all the diverse clients, mobile computing systems can only use small low-performance models for collaborative learning.

Federated Learning

Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning

no code implementations22 Nov 2023 Dengke Yan, Ming Hu, Zeke Xia, Yanxin Yang, Jun Xia, Xiaofei Xie, Mingsong Chen

However, due to data heterogeneity and stragglers, SFL suffers from the challenges of low inference accuracy and low efficiency.

Federated Learning

AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems

no code implementations22 Nov 2023 Chentao Jia, Ming Hu, Zekai Chen, Yanxin Yang, Xiaofei Xie, Yang Liu, Mingsong Chen

Although Federated Learning (FL) is promising to enable collaborative learning among Artificial Intelligence of Things (AIoT) devices, it suffers from the problem of low classification performance due to various heterogeneity factors (e. g., computing capacity, memory size) of devices and uncertain operating environments.

Federated Learning

DSAM-GN:Graph Network based on Dynamic Similarity Adjacency Matrices for Vehicle Re-identification

no code implementations25 Oct 2023 Yuejun Jiao, Song Qiu, Mingsong Chen, Dingding Han, Qingli Li, Yue Lu

Finally, the nodes and similarity adjacency matrices are fed into graph networks to extract more discriminative features for vehicle Re-ID.

Management Vehicle Re-Identification

WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks

no code implementations17 Oct 2023 Jun Xia, Zhihao Yue, Yingbo Zhou, Zhiwei Ling, Xian Wei, Mingsong Chen

Due to the popularity of Artificial Intelligence (AI) technology, numerous backdoor attacks are designed by adversaries to mislead deep neural network predictions by manipulating training samples and training processes.

Backdoor Attack SSIM

Continual Learning via Manifold Expansion Replay

no code implementations12 Oct 2023 Zihao Xu, Xuan Tang, Yufei Shi, Jianfeng Zhang, Jian Yang, Mingsong Chen, Xian Wei

To address this problem, we propose a novel replay strategy called Manifold Expansion Replay (MaER).

Continual Learning Management

EqGAN: Feature Equalization Fusion for Few-shot Image Generation

no code implementations27 Jul 2023 Yingbo Zhou, Zhihao Yue, Yutong Ye, Pengyu Zhang, Xian Wei, Mingsong Chen

Due to the absence of fine structure and texture information, existing fusion-based few-shot image generation methods suffer from unsatisfactory generation quality and diversity.

Generative Adversarial Network Image Generation

Hyperbolic Graph Diffusion Model

1 code implementation13 Jun 2023 Lingfeng Wen, Xuan Tang, Mingjie Ouyang, Xiangxiang Shen, Jian Yang, Daxin Zhu, Mingsong Chen, Xian Wei

In order to simultaneously utilize the data generation capabilities of diffusion models and the ability of hyperbolic embeddings to extract latent hierarchical distributions, we propose a novel graph generation method called, Hyperbolic Graph Diffusion Model (HGDM), which consists of an auto-encoder to encode nodes into successive hyperbolic embeddings, and a DM that operates in the hyperbolic latent space.

Graph Generation

FedMR: Federated Learning via Model Recombination

no code implementations18 May 2023 Ming Hu, Zhihao Yue, Zhiwei Ling, Yihao Huang, Cheng Chen, Xian Wei, Yang Liu, Mingsong Chen

Although Federated Learning (FL) enables global model training across clients without compromising their raw data, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance, especially for unevenly distributed data among clients.

Federated Learning

Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations

no code implementations16 Apr 2023 Jianzhang Zheng, Hao Shen, Jian Yang, Xuan Tang, Mingsong Chen, Hui Yu, Jielong Guo, Xian Wei

Motivated by the important role of ID, in this paper, we propose a novel deep representation learning approach with autoencoder, which incorporates regularization of the global and local ID constraints into the reconstruction of data representations.

Image Classification Representation Learning

A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold

no code implementations16 Feb 2023 Yanhong Fei, Xian Wei, Yingjie Liu, Zhengyu Li, Mingsong Chen

Although Deep Learning (DL) has achieved success in complex Artificial Intelligence (AI) tasks, it suffers from various notorious problems (e. g., feature redundancy, and vanishing or exploding gradients), since updating parameters in Euclidean space cannot fully exploit the geometric structure of the solution space.

Transfer Learning

CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning

no code implementations28 Jan 2023 Pengyu Zhang, Yingbo Zhou, Ming Hu, Xin Fu, Xian Wei, Mingsong Chen

Based on the concept of Continual Learning (CL), we prove that CyclicFL approximates existing centralized pre-training methods in terms of classification and prediction performance.

Continual Learning Federated Learning

HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation

no code implementations5 Dec 2022 Jun Xia, Yi Zhang, Zhihao Yue, Ming Hu, Xian Wei, Mingsong Chen

Federated learning (FL) has been recognized as a privacy-preserving distributed machine learning paradigm that enables knowledge sharing among various heterogeneous artificial intelligence (AIoT) devices through centralized global model aggregation.

Federated Learning Privacy Preserving

GitFL: Adaptive Asynchronous Federated Learning using Version Control

no code implementations22 Nov 2022 Ming Hu, Zeke Xia, Zhihao Yue, Jun Xia, Yihao Huang, Yang Liu, Mingsong Chen

Unlike traditional FL, the cloud server of GitFL maintains a master model (i. e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices.

Federated Learning Reinforcement Learning (RL)

FedCross: Towards Accurate Federated Learning via Multi-Model Cross Aggregation

no code implementations15 Oct 2022 Ming Hu, Peiheng Zhou, Zhihao Yue, Zhiwei Ling, Yihao Huang, Yang Liu, Mingsong Chen

Due to the remarkable performance in preserving data privacy for decentralized data scenarios, Federated Learning (FL) has been considered as a promising distributed machine learning paradigm to deal with data silos problems.

Federated Learning

FedMR: Fedreated Learning via Model Recombination

no code implementations16 Aug 2022 Ming Hu, Zhihao Yue, Zhiwei Ling, Xian Wei, Mingsong Chen

Worse still, in each round of FL training, FedAvg dispatches the same initial local models to clients, which can easily result in stuck-at-local-search for optimal global models.

Federated Learning Privacy Preserving

FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment

1 code implementation24 May 2022 Zhiwei Ling, Zhihao Yue, Jun Xia, Ming Hu, Ting Wang, Mingsong Chen

Along with the popularity of Artificial Intelligence (AI) and Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily increasing attentions as a promising distributed machine learning paradigm, which enables the training of a central model on for numerous decentralized devices without exposing their privacy.

Federated Learning

Model-Contrastive Learning for Backdoor Defense

1 code implementation9 May 2022 Zhihao Yue, Jun Xia, Zhiwei Ling, Ming Hu, Ting Wang, Xian Wei, Mingsong Chen

Due to the popularity of Artificial Intelligence (AI) techniques, we are witnessing an increasing number of backdoor injection attacks that are designed to maliciously threaten Deep Neural Networks (DNNs) causing misclassification.

Backdoor Attack backdoor defense +1

Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation

1 code implementation21 Apr 2022 Jun Xia, Ting Wang, Jiepin Ding, Xian Wei, Mingsong Chen

Due to the prosperity of Artificial Intelligence (AI) techniques, more and more backdoors are designed by adversaries to attack Deep Neural Networks (DNNs). Although the state-of-the-art method Neural Attention Distillation (NAD) can effectively erase backdoor triggers from DNNs, it still suffers from non-negligible Attack Success Rate (ASR) together with lowered classification ACCuracy (ACC), since NAD focuses on backdoor defense using attention features (i. e., attention maps) of the same order.

backdoor defense Knowledge Distillation +1

Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification

no code implementations11 Mar 2022 Jianzhang Zheng, Fan Yang, Hao Shen, Xuan Tang, Mingsong Chen, Liang Song, Xian Wei

We propose an algorithmic framework that leverages the advantages of the DNNs for data self-expression and task-specific predictions, to improve image classification.

Classification Image Classification

Machine Learning Empowered Intelligent Data Center Networking: A Survey

no code implementations28 Feb 2022 Bo Li, Ting Wang, Peng Yang, Mingsong Chen, Shui Yu, Mounir Hamdi

To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization.

BIG-bench Machine Learning Management

FedCAT: Towards Accurate Federated Learning via Device Concatenation

no code implementations23 Feb 2022 Ming Hu, Tian Liu, Zhiwei Ling, Zhihao Yue, Mingsong Chen

As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local data privacy.

Federated Learning

Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications

no code implementations29 Jan 2022 Tian Liu, Jiahao Ding, Ting Wang, Miao Pan, Mingsong Chen

However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure.

Federated Learning

O-ViT: Orthogonal Vision Transformer

no code implementations28 Jan 2022 Yanhong Fei, Yingjie Liu, Xian Wei, Mingsong Chen

Inspired by the tremendous success of the self-attention mechanism in natural language processing, the Vision Transformer (ViT) creatively applies it to image patch sequences and achieves incredible performance.

ViR:the Vision Reservoir

no code implementations27 Dec 2021 Xian Wei, Bin Wang, Mingsong Chen, Ji Yuan, Hai Lan, Jiehuang Shi, Xuan Tang, Bo Jin, Guozhang Chen, Dongping Yang

To address these problems, a novel method, namely, Vision Reservoir computing (ViR), is proposed here for image classification, as a parallel to ViT.

Classification Image Classification

Efficient Federated Learning for AIoT Applications Using Knowledge Distillation

no code implementations29 Nov 2021 Tian Liu, Zhiwei Ling, Jun Xia, Xin Fu, Shui Yu, Mingsong Chen

Inspired by Knowledge Distillation (KD) that can increase the model accuracy, our approach adds the soft targets used by KD to the FL model training, which occupies negligible network resources.

Federated Learning Knowledge Distillation

FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications

no code implementations28 Jun 2020 Yunfei Song, Tian Liu, Tongquan Wei, Xiangfeng Wang, Zhe Tao, Mingsong Chen

Along with the proliferation of Artificial Intelligence (AI) and Internet of Things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool Deep Neural Networks (DNNs) used by Industrial IoT (IIoT) applications.

Federated Learning

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