5 papers
Unlearning for Federated Online Learning to Rank: A Reproducibility Study
Yiling Tao, Shuyi Wang, Jiaxi Yang +1
This paper reports on findings from a comparative study on the effectiveness and efficiency of federated unlearning strategies within Federated Online Learning to Rank (FOLTR), wit…
Effective and secure federated online learning to rank
Shuyi Wang
Online Learning to Rank (OLTR) optimises ranking models using implicit user feedback, such as clicks. Unlike traditional Learning to Rank (LTR) methods that rely on a static set of…
How to Forget Clients in Federated Online Learning to Rank?
Shuyi Wang, Bing Liu, Guido Zuccon
Data protection legislation like the European Union's General Data Protection Regulation (GDPR) establishes the \textit{right to be forgotten}: a user (client) can request contribu…
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems
Shuyi Wang, Guido Zuccon
Federated online learning to rank (FOLTR) aims to preserve user privacy by not sharing their searchable data and search interactions, while guaranteeing high search effectiveness,…
Is Non-IID Data a Threat in Federated Online Learning to Rank?
Shuyi Wang, Guido Zuccon
In this perspective paper we study the effect of non independent and identically distributed (non-IID) data on federated online learning to rank (FOLTR) and chart directions for fu…