我们能在不泄露隐私的前提下预测您的下一步行动吗？
机器学习
2025-07-15 v1 人工智能
摘要
我们提出了FLLL3M——Federated Learning with Large Language Models for Mobility Modeling（大语言模型用于移动性建模的联邦学习）——一个面向下一位置预测（NxLP）的隐私保护框架。通过保留用户数据在本地并通过高效的外积机制利用LLM，FLLL3M实现高精度且资源需求低。它在Gowalla（Acc@1: 12.55, MRR: 0.1422）、WeePlace（10.71, 0.1285）、Brightkite（10.42, 0.1169）和FourSquare（8.71, 0.1023）上实现SOT结果，同时将参数减少最高可达45.6%，内存使用减少52.7%。
引用
@article{arxiv.2507.08843,
title = {Can We Predict Your Next Move Without Breaking Your Privacy?},
author = {Arpita Soni and Sahil Tripathi and Gautam Siddharth Kashyap and Manaswi Kulahara and Mohammad Anas Azeez and Zohaib Hasan Siddiqui and Nipun Joshi and Jiechao Gao},
journal= {arXiv preprint arXiv:2507.08843},
year = {2025}
}
备注
Accepted in the 17th International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2025), scheduled for 25 - 28 August 2025 in Ontario, Canada