PulseAugur
实时 20:23:58
None Building a privacy-preserving Federated Recommender system for mobile devices

开发出面向移动设备的注重隐私的联邦推荐系统

研究人员开发了一种新颖的两阶段联邦推荐系统,专为移动设备设计,并优先考虑用户隐私。该系统将敏感的移动上下文数据与非敏感的偏好数据分开,确保高度个人信息保留在用户设备上。基于云的协同过滤模型生成初步推荐,然后使用本地敏感数据进行设备端优化,仅传输模型更新。 AI

影响 为移动设备上的个性化内容交付引入了一种注重隐私的方法,解决了监管和用户期望的挑战。

排序理由 该集群包含一篇详细介绍新研究方法和系统实现的学术论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.LG TIER_1 · Aasheesh Singh ·

    Building a privacy-preserving Federated Recommender system for mobile devices

    arXiv:2605.22924v1 Announce Type: new 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 …

  2. arXiv cs.IR (Information Retrieval) TIER_1 · Aasheesh Singh ·

    Building a privacy-preserving Federated Recommender system for mobile devices

    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 pipe…