How to Forget Clients in Federated Online Learning to Rank?

• URL: http://arxiv.org/abs/2401.13410v1
• Date: Wed, 24 Jan 2024 12:11:41 GMT
• Title: How to Forget Clients in Federated Online Learning to Rank?
• Authors: Shuyi Wang, Bing Liu, Guido Zuccon
• Abstract summary: We study an effective and efficient unlearning method that can remove a client's contribution without compromising the overall ranker effectiveness. A key challenge is how to measure whether the model has instructd the contributions from the client $c*$ that has requested removal.
• Score: 34.5695601040165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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. In this paper, we study an effective and efficient unlearning method that can remove a client's contribution without compromising the overall ranker effectiveness and without needing to retrain the global ranker from scratch. A key challenge is how to measure whether the model has unlearned the contributions from the client $c^*$ that has requested removal. For this, we instruct $c^*$ to perform a poisoning attack (add noise to this client updates) and then we measure whether the impact of the attack is lessened when the unlearning process has taken place. Through experiments on four datasets, we demonstrate the effectiveness and efficiency of the unlearning strategy under different combinations of parameter settings.

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