Research on Open Innovation Intelligent Decision-Making of Cross-Border E-Commerce Based on Federated Learning

Zhang, Xiaodong; Guo, Chunrong

doi:10.1155/2022/9253634

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…To this end, FRSs can allow different data sources to collaborate in order to provide personalized recommendations to customers. A literature review on the application of FRSs in E-commerce revealed the various advantages [179][180][181]. First, FRSs can help reduce the cost of data access and storage.…”

Section: E-commercementioning

confidence: 99%

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

et al. 2023

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Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data. This approach allows for cooperation between data platforms while still complying with privacy regulations. In this paper, we explored the current state of research on FRSs, highlighting existing research issues and possible solutions. Specifically, we looked at how FRSs can be used to protect user privacy while still allowing organizations to benefit from the data they share. Additionally, we examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration. Finally, we discuss the challenges associated with developing and deploying FRSs in the real world and how these challenges can be addressed.

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Section: E-commercementioning

confidence: 99%

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

et al. 2023

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Closed-loop supply chain decision considering information reliability and security: should the supply chain adopt federated learning decision support systems?

et al. 2023

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The study considers the closed-loop supply chain (CLSC) decision using federated learning platform (FL platform), establishes a CLSC game model including one manufacturer, one retailer and one third party, analyzes the influence of FL platform on optimal decisions and revenues of CLSC system from the perspectives of information reliability, information security and investment cost, and establishes a cost-sharing and revenue-sharing contract (CSRS contract) to coordinate CLSC system. Results show that, the greater consumers' recognition of the remanufactured product, the higher its price and revenue; using FL platform can guarantee the degree of consumer information security, which in turn has a constructive effect on the CLSC; when the FL platform is not adopted, the manufacturer will raise more revenue by misrepresenting the manufacturing and remanufacturing costs; the FL platform would provide some incentive for manufacturers to disclose true cost information and improve the CLSC revenue; an increase in investment costs would lead to a reduction in revenue, so CLSC members would need to make investment decisions based on revenue comparisons before and after using the platform; finally, within the feasibility of the contract parameters, the CSRS contract both maximizes optimal revenue of CLSC system and improves revenue per member.

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Research on Open Innovation Intelligent Decision-Making of Cross-Border E-Commerce Based on Federated Learning

Cited by 2 publications

References 26 publications

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

Closed-loop supply chain decision considering information reliability and security: should the supply chain adopt federated learning decision support systems?

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