Building a privacy-preserving Federated Recommender system for mobile devices
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Computer Science > Machine Learning
Title:Building a privacy-preserving Federated Recommender system for mobile devices
Abstract:Serving personalized content on mobile devices has traditionally required pooling sensitive user data on centralized servers, a practice increasingly at odds with modern privacy expectations and geographical regulations. We present a two-stage federated recommendation system pipeline for mobile devices, built around a principled separation between non-sensitive user preference data and sensitive mobile context data that never leaves the device. The first stage runs a collaborative filtering model on non-sensitive app-context data in the cloud to generate a shortlist of relevant items. The second stage re-ranks these candidates on-device using sensitive mobile signals, with only model updates/gradients ever leaving the device. We validate the approach on MovieLens, UCI Human Activity Recognition, and a proprietary pilot dataset, and deliver a production-ready implementation as a Kotlin Multiplatform library deployable on Android and iOS.
| Comments: | this http URL. thesis, Université de Montréal, Department of Computer Science and Operations Research, 2024 |
| Subjects: | Machine Learning (cs.LG); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2605.22924 [cs.LG] |
| (or arXiv:2605.22924v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22924
arXiv-issued DOI via DataCite (pending registration)
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