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Electronics
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Security and Privacy in Distributed Machine Learning
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Security and Privacy in Distributed Machine Learning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 3309

Special Issue Editors

Dr. Chuan Zhang

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: applied cryptography; mobile crowdsourcing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangyun Tang

E-Mail Website
Guest Editor
School of Information Engineering, Minzu University of China, Beijing 100081, China
Interests: artificial intelligence security; federated learning; data security; privacy protection
Special Issues, Collections and Topics in MDPI journals
Dr. Yajie Wang

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: artificial intelligence security

Special Issue Information

Dear Colleagues,

Decentralized machine learning involves training models on distributed data sources, often without directly sharing the raw data. As the field of machine learning expands and embraces decentralized architectures, ensuring the security of decentralized machine learning becomes crucial. Decentralized machine learning security focuses on developing innovative techniques, algorithms, and frameworks to guarantee the privacy, integrity, and confidentiality of decentralized machine learning systems. It involves developing mechanisms to prevent privacy leakage and unauthorized access to sensitive data during the training process. Ensuring the reliability and trustworthiness of the participants is crucial to prevent adversarial attacks or manipulation of the training process. Additionally, decentralized machine learning security involves addressing resource constraints, optimizing computation and communication overhead, and mitigating the risks associated with system vulnerabilities and attacks.

Distributed Machine Learning Security is an important research area that aims to address the security challenges arising from the distributed nature of machine learning systems. By developing robust privacy-preserving techniques, protecting the integrity of models, and securing communication infrastructure, researchers are working towards enabling the widespread adoption of distributed machine learning in various sensitive domains while ensuring data privacy and model security.

ICA3PP (established in 1995) is a famous, worldwide event that covers many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems. The ICA3PP 2024 Workshop on Distributed Machine Learning Security, organized by the City University of Macau, is the 24th conference in this series. With the booming computing demands from every aspect of modern society, parallel processing has become increasingly critical and challenging. This conference provides a forum for academics and practitioners from all over the world to exchange ideas on improving the efficiency, performance, reliability, security, and interoperability of computing systems and applications.

The Special Issue primarily represents a collection of extended versions of selected papers presented at the ICA3PP 2024 Workshop on Distributed Machine Learning Security. However, papers not presented at the ICA3PP are also welcome. The topics of interest include, but are not limited to, the following:

  • Privacy-preserving techniques in decentralized machine learning;
  • Secure multi-party computation for distributed machine learning;
  • Federated learning;
  • Detection and mitigation of model poisoning attacks in decentralized settings;
  • Secure communication protocols for decentralized machine learning;
  • Trustworthiness and reputation management in decentralized machine learning;
  • Anomaly detection and intrusion detection in distributed machine learning;
  • Resource-constrained decentralized machine learning security;
  • Scalability and efficiency of security mechanisms in decentralized machine learning;
  • Secure aggregation methods for distributed machine learning;
  • Cryptographic protocols for secure data sharing in decentralized settings;
  • Adversarial attacks and defenses in decentralized machine learning;
  • Standardization and interoperability in decentralized machine learning security;
  • Real-world applications and case studies of decentralized machine learning security.

Dr. Chuan Zhang
Dr. Xiangyun Tang
Dr. Yajie Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • privacy-preserving techniques
  • secure multi-party computation
  • federated learning
  • poisoning attacks
  • communication protocols
  • secure aggregation methods
  • artificial intelligence security

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Published Papers (4 papers)

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Research

21 pages, 1411 KiB  
Article
Design of Self-Optimizing Polynomial Neural Networks with Temporal Feature Enhancement for Time Series Classification
by Yuqi Tang, Zhilei Xu and Wei Huang
Electronics 2025, 14(3), 465; https://doi.org/10.3390/electronics14030465 - 23 Jan 2025
Viewed by 467
Abstract
Time series classification is a significant and complex issue in data mining, it is prevalent across various fields and holds substantial research value. However, enhancing the classification rate of time series data remains a formidable challenge. Traditional time series classification methods often face [...] Read more.
Time series classification is a significant and complex issue in data mining, it is prevalent across various fields and holds substantial research value. However, enhancing the classification rate of time series data remains a formidable challenge. Traditional time series classification methods often face difficulties related to insufficient feature extraction or excessive model complexity. In this study, we propose a self-optimizing polynomial neural network with a temporal feature enhancement, which is referred to as OPNN-T. Existing classifiers based on polynomial neural networks (PNNs) struggle to achieve high-quality performances when dealing with time series data, primarily due to their inability to extract temporal information effectively. The goal of the proposed classifier is to enhance the nonlinear modeling capability for time series data, thereby improving the classification rate in practical applications. The key features of the proposed OPNN-T include the following: (1) A temporal feature module is employed to capture the dependencies in time series data, providing adaptability and flexibility in handling complex temporal patterns. (2) A polynomial neural network (PNN) is constructed using sub-datasets combined with three types of polynomial neurons, which enhances its nonlinear modeling capabilities across diverse scenarios. (3) A self-optimization mechanism is integrated into iteratively optimized sub-datasets, features, and polynomial types, resulting in significant improvements in the classification rate. The experimental results demonstrate that the proposed method achieves superior performances across multiple standard time series datasets, exhibiting higher classification accuracy and greater robustness than the existing classification models. Our research offers an effective solution for time series classification, and highlights the potential of polynomial neural networks in this field. Full article
(This article belongs to the Special Issue Security and Privacy in Distributed Machine Learning)
21 pages, 821 KiB  
Article
Federated Learning and Reputation-Based Node Selection Scheme for Internet of Vehicles
by Zhaoyu Su, Ruimin Cheng, Chunhai Li, Mingfeng Chen, Jiangnan Zhu and Yan Long
Electronics 2025, 14(2), 303; https://doi.org/10.3390/electronics14020303 - 14 Jan 2025
Viewed by 494
Abstract
With the rapid development of in-vehicle communication technology, the Internet of Vehicles (IoV) is gradually becoming a core component of next-generation transportation networks. However, ensuring the activity and reliability of IoV nodes remains a critical challenge. The emergence of blockchain technology offers new [...] Read more.
With the rapid development of in-vehicle communication technology, the Internet of Vehicles (IoV) is gradually becoming a core component of next-generation transportation networks. However, ensuring the activity and reliability of IoV nodes remains a critical challenge. The emergence of blockchain technology offers new solutions to the problem of node selection in IoV. Nevertheless, traditional blockchain networks may suffer from malicious nodes, which pose security threats and disrupt normal blockchain operations. To address the issues of low participation and security risks among IoV nodes, this paper proposes a federated learning (FL) scheme based on blockchain and reputation value changes. This scheme encourages active involvement in blockchain consensus and facilitates the selection of trustworthy and reliable IoV nodes. First, we avoid conflicts between computing power for training and consensus by constructing state-channel transitions to move training tasks off-chain. Task rewards are then distributed to participating miner nodes based on their contributions to the FL model. Second, a reputation mechanism is designed to measure the reliability of participating nodes in FL, and a Proof of Contribution Consensus (PoCC) algorithm is proposed to allocate node incentives and package blockchain transactions. Finally, experimental results demonstrate that the proposed incentive mechanism enhances node participation in training and successfully identifies trustworthy nodes. Full article
(This article belongs to the Special Issue Security and Privacy in Distributed Machine Learning)
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19 pages, 8047 KiB  
Article
Integrating Emotional Features for Stance Detection Aimed at Social Network Security: A Multi-Task Learning Approach
by Qiumei Pu, Fangli Huang, Fude Li, Jieyao Wei and Shan Jiang
Electronics 2025, 14(1), 186; https://doi.org/10.3390/electronics14010186 - 5 Jan 2025
Viewed by 576
Abstract
Stance detection seeks to identify the public’s position on a specific topic, providing critical insights for applications such as recommendation systems and rumor detection, which are essential for maintaining a secure social media environment. As one of China’s most influential social media platforms, [...] Read more.
Stance detection seeks to identify the public’s position on a specific topic, providing critical insights for applications such as recommendation systems and rumor detection, which are essential for maintaining a secure social media environment. As one of China’s most influential social media platforms, Weibo significantly shapes public discourse within its complex social network structure. Despite recent advancements in stance detection research on Weibo, many studies fail to adequately address the nuanced emotional features present in text, limiting detection accuracy and effectiveness, and potentially compromising online security. This paper proposes a stance detection approach based on multi-task learning that considers the influence of emotional features to tackle these challenges. Our method utilizes a RoBERTa pre-trained model in the shared layer to extract textual features for both stance detection and sentiment analysis. In the stance detection module, a BiLSTM model captures deeper temporal information, followed by three independent modules dedicated to extracting semantic features for specific stances. Concurrently, the sentiment analysis module employs a BiLSTM model to predict emotional polarity. The experimental results on the NLPCC2016-task4 dataset demonstrate that our approach outperforms existing methods, highlighting the effectiveness of integrating sentiment analysis with stance detection to enhance both accuracy and reliability, ultimately contributing to the security of social networks. Full article
(This article belongs to the Special Issue Security and Privacy in Distributed Machine Learning)
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20 pages, 1471 KiB  
Article
A Multi-Dimensional Reverse Auction Mechanism for Volatile Federated Learning in the Mobile Edge Computing Systems
by Yiming Hong, Zhaohua Zheng and Zizheng Wang
Electronics 2024, 13(16), 3154; https://doi.org/10.3390/electronics13163154 - 9 Aug 2024
Viewed by 1103
Abstract
Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure [...] Read more.
Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure efficient convergence and high test accuracy of the model has become an important issue. Traditional methods often use a reverse auction framework but ignore the consideration of client volatility. This paper proposes a multi-dimensional reverse auction mechanism (MRATR) that considers the uncertainty of client training time and reputation. First, we introduce reputation to objectively reflect the data quality and training stability of the client. Then, we transform the goal of maximizing social welfare into an optimization problem, which is proven to be NP-hard. Then, we propose a multi-dimensional auction mechanism MRATR that can find the optimal client selection and task allocation strategy considering clients’ volatility and data quality differences. The computational complexity of this mechanism is polynomial, which can promote the rapid convergence of FL task models while ensuring near-optimal social welfare maximization and achieving high test accuracy. Finally, the effectiveness of this mechanism is verified through simulation experiments. Compared with a series of other mechanisms, the MRATR mechanism has faster convergence speed and higher testing accuracy on both the CIFAR-10 and IMAGE-100 datasets. Full article
(This article belongs to the Special Issue Security and Privacy in Distributed Machine Learning)
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