How to Forget Clients in Federated Online Learning to Rank?

Shuyi Wang, Bing Liu, Guido Zuccon

Abstract: Data protection legislation like the European Union’s General Data Protection Regulation (GDPR) establishes the right to be forgotten: a user (client) can request contributions made using their data to be removed from learned models. In this paper, we study how to remove the contributions made by a client participating in a Federated Online Learning to Rank (FOLTR) system. In a FOLTR system, a ranker is learned by aggregating local updates to the global ranking model. Local updates are learned in an online manner at a client-level using queries and implicit interactions that have occurred within that specific client. By doing so, each client’s local data is not shared with other clients or with a centralised search service, while at the same time clients can benefit from an effective global ranking model learned from contributions of each client in the federation.