A scalable privacy-preserving information retrieval system based on federated optimization, on-device intelligence and semantic matching

基于联合优化、设备端智能和语义匹配的可扩展隐私保护信息检索系统

• 批准号: 19H04215
• 负责人: 于 海涛
• 金额: $ 10.15万
• 依托单位: University of Tsukuba
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2019
• 资助国家: 日本
• 起止时间: 2019-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-19H04215/
• 关键词: privacy-preserving; federated learning; semantic matching; online learning-to-rank; interactive search; on-device learning; Metric Optimization; Policy Gradient; Differential Privacy; Privacy-preserving IR; Federated Optimization; On-device Lea

This year our main task is to develop the federated information retrieval model based on a large-scale dataset. To this end, we firstly view information retrieval as an interactive process between users and the search engine system rather than an independent ranking per query. Secondly, we explore how to effectively integrate online learning-to-rank and federated learning. Specifically, online learning-to-rank enables us to cope with the aforementioned interactive process. Federated learning enables us to deal with the privacy issue by learning the ranking model in an on-device manner. Based on a series of experiments on several benchmark ranking datasets, our experimental results show that this direction is applicable.

今年，我们的主要任务是开发基于大规模数据集的联合信息检索模型。为此，我们首先将信息检索视为用户与搜索引擎系统之间的交互过程，而不是每个查询的独立排名。其次，我们探讨了如何有效地整合在线学习到等级和联合学习的方法。具体来说，在线学习到级别使我们能够应对上述交互式过程。联合学习使我们能够以现实方式学习排名模型来处理隐私问题。基于对几个基准排名数据集的一系列实验，我们的实验结果表明该方向适用。

项目成果

FLAME: Differentially Private Federated Learning in the Shuffle Model

• DOI: 10.1609/aaai.v35i10.17053
• 发表时间: 2020-09
• 作者: Ruixuan Liu;Yang Cao;Hong Chen;Ruoyang Guo;Masatoshi Yoshikawa

Dalian University of Technology(中国)

大连理工大学（中国）

FL-Market: Trading Private Models in Federated Learning

• DOI: 10.1109/bigdata55660.2022.10020232
• 发表时间: 2021-06
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Shuyuan Zheng;Yang Cao;Masatoshi Yoshikawa;Huizhong Li;Qiang Yan

TUA1 at eRisk 2022: Exploring Affective Memories for Early Detection of Depression

• 发表时间: 2022
• 作者: Xin Kang;Rongyu Dou;Haitao Yu

PPIR

PPIR