OES-Fed: a federated learning framework in vehicular network based on noise data filtering

Lei, Yuan; Wang, Shir Li; Caiyu, Su; Ng, Theam Foo

doi:10.7717/peerj-cs.1101

Cited by 19 publications

(3 citation statements)

References 42 publications

(50 reference statements)

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“…We use the CIFAR-10 and vehicle image datasets for experiments, both of which are publicly available. CIFAR-10 is a popular dataset for experiments in the field of federated learning [34][35][36]. It contains 10 kinds of color pictures, such as planes, cars, ship, and truck.…”

Section: Dataset Descriptionmentioning

confidence: 99%

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

et al. 2022

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With the rapid increase of data, centralized machine learning can no longer meet the application requirements of the Internet of Vehicles (IoV). On the one hand, both car owners and regulators pay more attention to data privacy and are unwilling to share data, which forms the isolated data island challenge. On the other hand, the incremental data generated in IoV are massive and diverse. All these issues have brought challenges of data increment and data diversity. The current common federated learning or incremental learning frameworks cannot effectively integrate incremental data with existing machine learning (ML) models. Therefore, this paper proposes a Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (Fed-IW&DS). In Fed-IW&DS, a vehicle diversity selection algorithm was proposed, which uses a variety of performance indicators to calculate diversity scores, effectively reducing homogeneous computing. Also, it proposes a vehicle federated incremental algorithm that uses an improved arctangent curve as the decay function, to realize the rapid fusion of incremental data with existing ML models. Moreover, we have carried out several sets of experiments to test the validity of the proposed Fed-IW&DS framework’s performance. The experimental results show that, under the same global communication round and similar computing time, the Fed-IW&DS framework has significantly improved performance in all aspects compared to the frameworks FED-AVG, FED-SGD, FED-prox & the decay functions linear, square curve and arc tangent. Specifically, the Fed-IW&DS framework improves the Acc (accuracy), loss (loss), and Matthews correlation coefficient (MCC) by approximately 32%, 83%, and 66%, respectively. This result shows that Fed-IW&DS is a more reliable solution than the common frameworks of federated learning, and it can effectively deal with the dynamic incremental data in the IoV scenario. Our findings should make a significant contribution to the field of federated learning.

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Section: Dataset Descriptionmentioning

confidence: 99%

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

et al. 2022

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“…To address these challenges, we employ federated learning, Generative Adversarial Networks (GANs), and multi-center federated learning. Federated learning provides a secure, decentralized solution for exchanging user behavior data [9,10]. However, due to the unique behavior patterns of each user and the unique features of the data stored on their devices, the data distributions on individual user devices may differ from the overall data distribution, resulting in non-IID data distribution in targeted advertising scenarios [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Empowering precise advertising with Fed-GANCC: A novel federated learning approach leveraging Generative Adversarial Networks and group clustering

Su,

Wei,

Lei

et al. 2024

PLoS ONE

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In the realm of targeted advertising, the demand for precision is paramount, and the traditional centralized machine learning paradigm fails to address this necessity effectively. Two critical challenges persist in the current advertising ecosystem: the data privacy concerns leading to isolated data islands and the complexity in handling non-Independent and Identically Distributed (non-IID) data and concept drift due to the specificity and diversity in user behavior data. Current federated learning frameworks struggle to overcome these hurdles satisfactorily. This paper introduces Fed-GANCC, an innovative federated learning framework that synergizes Generative Adversarial Networks (GANs) and Group Clustering. The framework incorporates a user data augmentation algorithm predicated on adversarial generative networks to enrich user behavior data, curtail the impact of non-uniform data distribution, and enhance the applicability of the global machine learning model. Unlike traditional approaches, our framework offers user data augmentation algorithms based on adversarial generative networks, which not only enriches user behavior data but also reduces the challenges posed by non-uniform data distribution, thereby enhancing the applicability of the global machine learning (ML) model. The effectiveness of Fed-GANCC is distinctly showcased through experimental results, outperforming contemporary methods like FED-AVG and FED-SGD in terms of accuracy, loss value, and receiver operating characteristic (ROC) indicators within the same computing time. Experimental results vindicate the effectiveness of Fed-GANCC, revealing substantial enhancements in accuracy, loss value, and receiver operating characteristic (ROC) metrics compared to FED-AVG and FED-SGD given the same computational time. These outcomes underline Fed-GANCC’s exceptional prowess in mitigating issues such as isolated data islands, non-IID data, and concept drift. With its novel approach to addressing the prevailing challenges in targeted advertising such as isolated data islands, non-IID data, and concept drift, the Fed-GANCC framework stands as a benchmark, paving the way for future advancements in federated learning solutions tailored for the advertising domain. The Fed-GANCC framework promises to offer pivotal insights for the future development of efficient and advanced federated learning solutions for targeted advertising.

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“…Machine learning [3] has been applied to risk assessment [4], financial performance [5], fault network management [6], and other fields due to its ability to constantly learn and adapt to the development of enterprises.…”

Section: Introductionmentioning

confidence: 99%

Methods to Improve Company Maturity Based on Analytic Hierarchy Process, Entropy Weight Method and Visual Radar Map

Wang

2023

Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023)

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With the development of 5G, the Internet of Things, and other technologies, data needs to be analyzed and sorted in a larger way, so as to obtain more valuable information from it. In order to address the problem that the maturity of different companies is difficult to analyze due to business factors, in this paper, we show the AHP-EWM combined weight analysis method and visual radar to find the model of the optimal solution, and design a system of maturity index that is suitable for companies. Subsequently, the degree of KPI of the company is shown through a radar map, and find out the optimal direction of the internal system of enterprise development. By increasing the weight ratio of optimal development factors, the maturity of the company can be improved. Finally, this model is used to simulate the application degree of port industries of different sizes and different enterprises. The results of the radar map show that even enterprises of different sizes and properties can be normalized by this model according to their internal KPIs, and the optimization suggestions given by the radar map can improve the maturity of the company, which is of great significance for the company to integrate internal resources and find the development direction of the company.

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OES-Fed: a federated learning framework in vehicular network based on noise data filtering

Cited by 19 publications

References 42 publications

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

Empowering precise advertising with Fed-GANCC: A novel federated learning approach leveraging Generative Adversarial Networks and group clustering

Methods to Improve Company Maturity Based on Analytic Hierarchy Process, Entropy Weight Method and Visual Radar Map

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