Federated Learning Approaches for Privacy-Preserving Recommendation Systems in Large-Scale Cloud-Based Retail Ecosystems

Johanna Traven Nino

Federated Learning Approaches for Privacy-Preserving Recommendation Systems in Large-Scale Cloud-Based Retail Ecosystems

Authors

Johanna Traven Nino

Federated Machine Learning Engineer – Privacy-Preserving Recommendations in Cloud Retail, France

Keywords:

Federated Learning, Recommendation Systems, Privacy Preservation, Cloud Retail, Edge Computing, Data Security, Machine Learning, Collaborative Filtering

Synopsis

In the era of personalized digital services, recommendation systems (RS) are pivotal to enhancing user engagement and driving sales in retail ecosystems. However, the use of centralized user data raises significant privacy concerns. Federated Learning (FL) emerges as a transformative solution, enabling collaborative model training without exposing sensitive data. This paper explores the integration of FL into large-scale cloud-based retail recommendation systems, emphasizing privacy preservation, scalability, and efficiency. We evaluate state-of-the-art FL methods, analyze their performance, and present a prototype framework demonstrating their practical applicability. Experimental results indicate improved privacy guarantees with competitive model accuracy. Our study concludes with insights on future directions for federated RS development in dynamic retail environments.

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