Reputation-Based Regional Federated Learning for Knowledge Trading in Blockchain-Enhanced IoV

Zou, Yue; Shen, Fei; Yan, Feng; Lin, Jing; Qiu, Yunzhou

doi:10.1109/wcnc49053.2021.9417347

Cited by 34 publications

(28 citation statements)

References 12 publications

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“…FL is beneficial to many scenarios in ITS or IoV, e.g. optimized routing, congestion control and object detection for autonomous driving ( [37], [44], [49], [61], [63]). Vehicles collect local information and train local models with collected data.…”

Section: Rq 1: Overviewmentioning

confidence: 99%

“…Voting on the next leader (aggregator) [51], [52] or on each others contributions [58], [59], [68] further democratizes the process. Beyond explicit coordination operations like voting or leader selection, the implicit function of storing crucial information and data for the FL process, [30], [39], [56], [61] , verifying correctness of updates [34], [54] or keeping the registry of active members [30], [31], [38], [39], [58], [59] is crucial for the FL workflow and implies coordination through blockchain as an always accessible, verifiable, transparent and immutable infrastructure. 3) Payment Many general-purpose blockchain systems include cryptocurrency capabilities and therefore allow for the incorporation of instant, automatic and deterministic payment schemes defined by the smart contract's business logic.…”

Section: Rq 2-2: How Is Blockchain Applied Within the Flf And What Op...mentioning

confidence: 99%

“…[40], [44], [45], [58], [59], [63]), data acquisition (e.g. pictures and sensor readings [61], [62], [63]) and privacy leakage due to model updates (e.g. [62], [63], [65]).…”

Section: Rq 3-1: How Are Incentive Mechanisms Analyzed?mentioning

confidence: 99%

“…Wang et al propose a Q-learning-based approach to determine the optimal prices so that utilities are maximized via iterative learning processes [63]. Similarly, Zou et al derive the optimal prices for workers with first-and second-order conditions when the value of data, transmission quality and communication delay are the factors to determine their competitiveness and costs [61]. Hu et al propose a twostage optimization method to determine the optimal values on data and their prices in order by solving an Euler-Lagrange equation of their utilities.…”

Section: It Is Up To Us To Determine What To Analyze With Utilitiesmentioning

confidence: 99%

See 3 more Smart Citations

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt¹,

Heyer²,

Toyoda³

et al. 2022

Preprint

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The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants have to be incentivized. This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific databases. Finally, 40 articles remained after a systematic review and filtering process for in-depth examination. Although having massive potential to direct the future of a more distributed and secure AI, none of the analyzed FLF is production-ready. The approaches vary heavily in terms of use-cases, system design, solved issues and thoroughness. We are the first to provide a systematic approach to classify and quantify differences between FLF, exposing limitations of current works and derive future directions for research in this novel domain.

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Section: Rq 1: Overviewmentioning

confidence: 99%

Section: Rq 2-2: How Is Blockchain Applied Within the Flf And What Op...mentioning

confidence: 99%

“…[40], [44], [45], [58], [59], [63]), data acquisition (e.g. pictures and sensor readings [61], [62], [63]) and privacy leakage due to model updates (e.g. [62], [63], [65]).…”

Section: Rq 3-1: How Are Incentive Mechanisms Analyzed?mentioning

confidence: 99%

Section: It Is Up To Us To Determine What To Analyze With Utilitiesmentioning

confidence: 99%

See 2 more Smart Citations

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt¹,

Heyer²,

Toyoda³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) with artificial intelligence and deep neural networks (DNN) is making a major impact on the IoV revolution including 6G communications [122]. 6G communication link and system solutions are constructed using artificial intelligence and machine learning.…”

Section: Future Directionsmentioning

confidence: 99%

Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends

et al. 2021

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The Internet of Vehicles (IoV) and Vehicle‐to‐Everything (V2X) concept have emerged from IoT technology, which refers to connecting many vehicles with various applications to the internet. The 5G new radio is based on a cloud‐radio access network (CRAN), considered as the communication infrastructure for IoV. However, due to the significant challenges and issues, researchers have been working on IoV and V2X. One of the main challenges for V2X is resource allocation and management for a high‐speed vehicular environment. This paper discusses and provides complete detail for resource allocation and management for IoV over 5G RAN networks focusing on artificial intelligence techniques. The paper also presented reviews on integrating the multi‐layers of vehicular network architecture with AI strategy to identify advancement and future directions for resource allocation and management issues.

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RepBFL: Reputation Based Blockchain-Enabled Federated Learning Framework for Data Sharing in Internet of Vehicles

Chen

et al. 2022

Parallel and Distributed Computing, Applications and Technologies

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Reputation-Based Regional Federated Learning for Knowledge Trading in Blockchain-Enhanced IoV

Cited by 34 publications

References 12 publications

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends

RepBFL: Reputation Based Blockchain-Enabled Federated Learning Framework for Data Sharing in Internet of Vehicles

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