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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33664 条记录，以下是261-270 订阅

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Enhancing Federated learning Robustness in Non-IID data Environments via MMD-Based Distribution Alignment 25th

Enhancing Federated Learning Robustness in Non-IID Data Env...

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25th international conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Ma, Xiao Shen, Hong Lyu, Wenqi Ke, Wei Faculty of Applied Sciences Macao Polytechnic University China School of Engineering and Technology Central Queensland University Rockhampton Australia

ISBN: (纸本)9789819642069

Federated learning(FL), due to its distributed nature, is highly susceptible to malicious attacks. Although various Byzantine-robust FL methods exist, they often fail to maintain robustness in practical scenarios due to the non-independent and identically distributed (Non-IID) nature of client data. Moreover, existing FL methods often suffer from weight divergence caused by heterogeneous data distributions across clients. To address these issues, we propose a novel federated learning framework that aligns local data distributions across different clients to enhance robustness for Non-IID data in adversarial environments. It contains a feature transformation layer that incorporates Maximum Mean Discrepancy (MMD) as a regularization term to avoid weight divergence through aligning local and global data distributions without sharing raw data. Our approach dynamically updates the statistical information of both local and global data, including the mean and variance, ensuring that local models are closely aligned with the global model throughout training. Experimental results on MNIST and CIFAR-10 datasets demonstrate that our proposed framework significantly improves robustness both in the absence of attacks and against untargeted attacks such as sign-flipping and additive noise. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Center-Based Approximation of a Drifting Distribution 36

Center-Based Approximation of a Drifting Distribution

引用

36th international conference on Algorithmic learning Theory, ALT 2025

作者： Mazzetto, Alessio Ceccarello, Matteo Pietracaprina, Andrea Pucci, Geppino Upfal, Eliezer Brown University United States University of Padova Italy

We present a novel technique for computing a center-based approximation of a drifting distribution. Given k ≥ 1 and a stream of data, whose distribution is changing over time, the goal is to compute, at each step, the best k centers representation of the current distribution, despite possibly having only a single sample from the most recent distribution. In data mining, this is traditionally attempted through the sliding-window mechanism, where the analysis is performed on the most recent fixed-size segment of the data. The problems with this approach are twofold: (1) setting the correct window size is challenging;and (2) a fixed window size cannot effectively track changes in the distribution happening at variable speed. In this paper, we propose a new methodology that dynamically adjusts the window size based on the recent drift of the data. The challenge is that it is not possible to explicitly estimate the drift, as we may have only a single data point from each distribution. Our main contribution lies in providing a rigorous mathematical analysis, establishing both an upper bound via a dynamic window size algorithm, and a lower bound that shows the tightness of our approach. © 2025 A. Mazzetto, M. Ceccarello, A. Pietracaprina, G. Pucci & E. Upfal.

关键词： data mining

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Weibo-FA: A Benchmark dataset for Fake Account Detection in Weibo Platform 25th

Weibo-FA: A Benchmark Dataset for Fake Account Detection in ...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Li, Zhiqi Fang, Weidong Zhang, Wuxiong Science and Technology on Microsystem Laboratory Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai201899 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Shanghai Research and Development Center for Micro-Nano Electronics Shanghai201210 China

ISBN: (纸本)9789819605750

Weibo is one of the top social media platforms in China, similar to X/Twitter. There are many fake accounts on Weibo who may have various negative impacts on the platform and community. However, most existing work on fake user datasets and detection models is concentrated on X/Twitter, with little consideration given to Weibo. In this paper, we first review some methods for detecting fake accounts on social networking platforms. Secondly, we introduce a dataset called Weibo-FA dataset collected from the Weibo platform, which contains profile information of both fake and genuine accounts. Then Weibo-FA dataset is compared with a baseline dataset using several basic machine learning models and neural networks to evaluate its applicability in traditional models. Subsequently, the Weibo-FA dataset is employed to train two state-of-the-art neural network-based models to demonstrate its suitability for advanced models. The test results are compared with related work. Regarding the results, they indicate that the proposed Weibo-FA dataset exhibits good usability and completeness. It not only enhances the performance of traditional simple models but also achieves high accuracy in detecting fake accounts for advanced complex models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Secure Resource Allocation via Constrained Deep Reinforcement learning 6th

Secure Resource Allocation via Constrained Deep Reinforceme...

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6th international conference on machine learning for Cyber Security, ML4CS 2024

作者： Sun, Jianfei Gao, Qiang Wu, Cong Li, Yuxian Wang, Jiacheng Niyato, Dusit School of Computing and Information Systems Singapore Management University Singapore188065 Singapore School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9789819645657

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios-including different task loads, data sizes, and MEC capacities-show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO ’s potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Embedding data in Bitcoin Transaction Values with Restricted Boltzmann machine 2nd

Embedding Data in Bitcoin Transaction Values with Restrict...

引用

2nd international conference on data Security and Privacy Protection, DSPP 2024

作者： An, Jiayi Tian, Haibo Guangdong Province Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9789819785391

Constructing covert channels on blockchains has recently become a significant research focus. A major challenge lies in embedding data into blockchain transactions, while maintaining strong concealment. This study focuses on utilizing blockchain transaction values as a carrier, exploring methods to ensure that the distribution of values with embedded data mimics that of standard transactions. Initially, we gathered authentic blockchain transaction data, conducted a frequency analysis on the transaction values, and filtered out abnormal values to establish a baseline normal value dataset. Subsequently, we employed a Restricted Boltzmann machine (RBM) to generate a simulated value dataset that mirrors the distribution of the normal dataset, facilitating the embedding of data within the simulated value dataset. By fine-tuning the number of bits embedded in the values, we achieved a value dataset with embedded data that closely resembles the normal distribution. We have calculated the Kullback-Leibler Divergence and the Kolmogorov-Smirnov Test between the value dataset with embedded data and the normal value dataset under various embedding scenarios, revealing that the changes to the original dataset are minimal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Normal distribution

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Refiner: Fine-grained Cross-modal Concepts Refinement for Compositional Zero-Shot learning

Refiner: Fine-grained Cross-modal Concepts Refinement for Co...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Xiao Jing, Haodong Chen, Hui Ma, Yongqiang Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi China

ISBN: (纸本)9798350368741

Recent Compositional Zero-Shot learning (CZSL) methods increasingly adopt the pre-trained vision-language models to capture the contextual relations between image and text spaces. However, the single-class-token design from Transformer-based encoder inevitably captures contextual information from unrelated objects and background, thus hindering the modeling of fine-grained class-specific visual features. Suffering from cross-modal gap, prior methods also struggle to improve compositional recognition performance. To address these issues, we propose a fine-grained cross-modal concepts refinement framework, termed as Refiner, which comprises two pivotal components: (i) the fine-grained concepts refinement of image embeddings to capture state-object context within visual scenes, and (ii) the cross-modal information fusion to mitigate the modality gap. By leveraging learnable query vectors to capture region-specific semantic information pertinent to composition labels, our approach refines visual representations with fine-grained state-object context information. As for cross-modal information fusion, we construct a robust image-to-text mapping by aligning visual embeddings with states, objects, and compositions, respectively. Extensive experiments demonstrate that our Refiner achieves new state-of-the-art performance across all popular benchmarks in both closed- and open-world settings. © 2025 IEEE.

关键词： Compositional Zero-shot learning Cross-Modal Fusion Fine-Grained Refinement Multimodal Models

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Towards Robust Policy: Enhancing Offline Reinforcement learning with Adversarial Attacks and Defenses 4th

Towards Robust Policy: Enhancing Offline Reinforcement Learn...

引用

4th international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Nguyen, Thanh Luu, Tung M. Ton, Tri Yoo, Chang D. Daejeon Korea Republic of

ISBN: (纸本)9789819787043

Offline reinforcement learning (RL) addresses the challenge of expensive and high-risk data exploration inherent in RL by pre-training policies on vast amounts of offline data, enabling direct deployment or fine-tuning in real-world environments. However, this training paradigm can compromise policy robustness, leading to degraded performance in practical conditions due to observation perturbations or intentional attacks. While adversarial attacks and defenses have been extensively studied in deep learning, their application in offline RL is limited. This paper proposes a framework to enhance the robustness of offline RL models by leveraging advanced adversarial attacks and defenses. The framework attacks the actor and critic components by perturbing observations during training and using adversarial defenses as regularization to enhance the learned policy. Four attacks and two defenses are introduced and evaluated on the D4RL benchmark. The results show the vulnerability of both the actor and critic to attacks and the effectiveness of the defenses in improving policy robustness. This framework holds promise for enhancing the reliability of offline RL models in practical scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Development Trends of Big data and Artificial Intelligence in Virtual Simulation Technology 14th

Development Trends of Big Data and Artificial Intelligence i...

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14th international conference on Frontier Computing, FC 2024

作者： Yang, Yidong Wang, Lanfeng Wei, Yuhua Guangzhou Huali Science and Technology Vocational College Guangzhou China

ISBN: (纸本)9789819627974

With the development of information technology, big data technology and artificial intelligence have become important directions for informationization in the world today. Big data technology can help people better process, manage, and utilize massive amounts of data, while artificial intelligence can make computers more intelligent, achieving automated and intelligent processing and decision-making. Combining the two can achieve more powerful information processing and application capabilities, with broad application prospects. The application of artificial intelligence in virtual simulation technology has also played a crucial role. Artificial intelligence technology can simulate human intelligent behavior and provide more intelligent support for virtual simulation technology. Through artificial intelligence algorithms, virtual simulation can be more automated and intelligent for simulation and prediction, improving the efficiency and accuracy of virtual simulation. In addition, artificial intelligence can also help virtual simulation technology achieve autonomous learning and optimization, continuously improving its performance and functionality. Based on the above issues, this article explores a virtual gesture interaction perception simulation model based on CNN (Convolutional Neural Networks) algorithm, extracts gesture datasets using artificial intelligence algorithms, and estimates gesture coordinates. The experimental results indicate that the sample sizes are 100, 200, and 300. The time for virtual gesture interaction perception based on CNN algorithm is 7.39ms, 10.87ms, and 14.67ms, which are 4.37ms, 5.61ms, and 7.07ms less than RNN (Recurrent Neural Network) algorithm, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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Research on Two-Level Master Production Scheduling Based on Q learning 16th

Research on Two-Level Master Production Scheduling Based on ...

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16th international conference on Modelling, Identification and Control, ICMIC 2024

作者： Han, Zhonghua Hui, Yingmeng School of Electrical and Control Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China

ISBN: (纸本)9789819617760

With the progress of industrialization of the food processing industry, related production control software has been rapidly applied in this industry, but the master production scheduling (MPS) software of other industries cannot meet the characteristics of the MPS for food processing industry. In view of the above problems, a two-level MPS method based on Q learning is proposed, which adopts a multi-level method and is divided into bloc level, company level and workshop level. According to the order requirements and related constraints, the customer order is converted into the corresponding weekly production schedule and issued to the company level. The company level adopts the state and action of the Q learning algorithm as the basis, combined with the evenly distributed remainder method to make a choice. Then according to the reward function, the trained Q value is updated to the Q table, and the scheduling is selected. The scheduling selection is formulated into a master plan, and the compiled master plan is issued to the workshop level. After receiving the plan, the workshop level conducts scheduling according to the plan. The simulation test using sample data of food processing enterprises verified that the utilization of multi-level, Q learning algorithm and evenly distributed remainder method can solve the problem of MPS in the food processing industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Food industry

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Real-Time Encrypted Traffic Classification in Programmable Networks with P4 and machine learning

Real-Time Encrypted Traffic Classification in Programmable N...

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作者： Akem, Aristide Tanyi-Jong Fraysse, Guillaume Fiore, Marco Networks Data Science Group IMDEA Networks Institute Madrid Spain Department of Telematic Engineering Universidad Carlos III de Madrid Madrid Spain SMART Team Orange Innovation Networks Châtillon France

Network traffic encryption has been on the rise in recent years, making encrypted traffic classification (ETC) an important area of research. machine learning (ML) methods for ETC are widely regarded as the state of the art. However, most existing solutions either rely on offline ETC based on collected network data or on online ETC with models running in the control plane of software-defined networks, all of which do not run at line rate and would not meet the strict requirements of ultra-low-latency applications in modern networks. This work exploits recent advances in data plane programmability to achieve real-time ETC in programmable switches at line rate, with high throughput and low latency. An extensive analysis is first conducted to show how tree-based models excel in ETC on various datasets. Then, a workflow is proposed for in-switch ETC with tree-based models. The proposed workflow builds on (i) an ETC-aware random forest (RF) modelling process where only features based on packet size and packet arrival times are used and (ii) an encoding of the trained RF model into off-the-shelf P4-programmable switches. The performance of the proposed in-switch ETC solution is evaluated on three use cases based on publicly available encrypted traffic datasets. Experiments are then conducted in a real-world testbed with Intel Tofino switches, in the presence of high-speed background traffic. Results show how the solution achieves high classification accuracy of up to 95% in QUIC traffic classification, with submicrosecond delay while consuming less than 10% on average of the total hardware resources available on the switch. © 2025 The Author(s). international Journal of Network Management published by John Wiley & Sons Ltd.

关键词： Time switches

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