Building a privacy-preserving Federated Recommender system for mobile devices

• URL: http://arxiv.org/abs/2605.22924v2
• Date: Wed, 27 May 2026 18:29:59 GMT
• Title: Building a privacy-preserving Federated Recommender system for mobile devices
• Abstract summary: We present a two-stage federated recommendation system pipeline for mobile devices.<n>The first stage runs a collaborative filtering model on non-sensitive app-context data in the cloud to generate a shortlist of relevant items.<n>The second stage re-ranks these candidates on-device using sensitive mobile signals, with only model updates/gradients ever leaving the device.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Towards Context-aware Mobile Privacy Notice: Implementation of A Deployable Contextual Privacy Policies Generator [14.455554127094791]
  We present PrivScan, the first deployable Contextual Privacy Policies Software Development Kit (SDK) for Android.<n>It captures live app screenshots to identify GUI elements associated with types of personal data and displays CPPs in a concise, user-facing format.<n>A feasibility-oriented evaluation shows an average execution time of 9.15,s, demonstrating the practicality of our approach.
  arXiv  Detail & Related papers  (2025-09-26T20:26:32Z)
• CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering [80.54309860395763]
  CoSteer is a novel collaborative framework that enables decoding-time personalization through localized delta steering.<n>We formulate token-level optimization as an online learning problem, where local delta vectors dynamically adjust the remote LLM's logits.<n>This approach preserves privacy by transmitting only the final steered tokens rather than raw data or intermediate vectors.
  arXiv  Detail & Related papers  (2025-07-07T08:32:29Z)
• MobileA3gent: Training Mobile GUI Agents Using Decentralized Self-Sourced Data from Diverse Users [52.696186533146516]
  MobileA3gent is a collaborative framework that trains mobile GUI Agents using decentralized self-sourced data.<n>MobileA3gent achieves superior performance over traditional approaches at only 1% of the cost.
  arXiv  Detail & Related papers  (2025-02-05T08:26:17Z)
• Personalized Language Model Learning on Text Data Without User Identifiers [79.36212347601223]
  We propose to let each mobile device maintain a user-specific distribution to dynamically generate user embeddings.<n>To prevent the cloud from tracking users via uploaded embeddings, the local distributions of different users should either be derived from a linearly dependent space.<n> Evaluation on both public and industrial datasets reveals a remarkable improvement in accuracy from incorporating anonymous user embeddings.
  arXiv  Detail & Related papers  (2025-01-10T15:46:19Z)
• Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis [27.792167318819068]
  We propose a novel framework to select and store the most representative data online in a self-supervised way. Our experiments show that the proposed framework achieves the best user-specific content-generating capability (accuracy) and fine-tuning speed (performance) compared with vanilla baselines.
  arXiv  Detail & Related papers  (2023-11-21T01:34:02Z)
• Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction [52.16923290335873]
  We propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. We modify the structure of the classical matrix factorization model and update the training procedure to sequential learning. One more ingredient of the proposed approach is a new privacy mechanism that guarantees the protection of the sent data from the users to the remote server.
  arXiv  Detail & Related papers  (2023-02-05T10:29:57Z)
• Federated Split GANs [12.007429155505767]
  We propose an alternative approach to train ML models in user's devices themselves. We focus on GANs (generative adversarial networks) and leverage their inherent privacy-preserving attribute. Our system preserves data privacy, keeps a short training time, and yields same accuracy of model training in unconstrained devices.
  arXiv  Detail & Related papers  (2022-07-04T23:53:47Z)
• Federated Learning-based Active Authentication on Mobile Devices [98.23904302910022]
  User active authentication on mobile devices aims to learn a model that can correctly recognize the enrolled user based on device sensor information. We propose a novel user active authentication training, termed as Federated Active Authentication (FAA) We show that existing FL/SL methods are suboptimal for FAA as they rely on the data to be distributed homogeneously.
  arXiv  Detail & Related papers  (2021-04-14T22:59:08Z)
• Enabling Seamless Device Association with DevLoc using Light Bulb Networks for Indoor IoT Environments [0.7519872646378835]
  System named DevLoc takes advantage of ubiquitous light sources around us to perform continuous and seamless device grouping. We support two modes of device associations between different entities: device-to-device and device-to-area. We analyze patterns of device associations to improve the data privacy by recognizing semantic device groups, such as personal and stranger's devices.
  arXiv  Detail & Related papers  (2020-05-15T18:33:42Z)
• Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem [55.21502268698577]
  This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We provide a novel Dual User-Adaptation framework (DUA) to explore the problem. This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device.
  arXiv  Detail & Related papers  (2020-03-30T09:35:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.