# How to Forget Clients in Federated Online Learning to Rank?

arXiv: https://arxiv.org/abs/2401.13410 · HTML: https://mlsanity.com/paper/2401.13410/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security.
- Builds on 31 of its 51 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Placed in

- LLM safety and security / [Federated unlearning and poisoning](https://mlsanity.com/safety/federated-unlearning-and-poisoning/)

## Builds on (most cited first, 30 of 31)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019)
- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [A Little Is Enough: Circumventing Defenses For Distributed Learning](https://mlsanity.com/paper/1902.06156/) (2019)
- [When Machine Unlearning Jeopardizes Privacy](https://mlsanity.com/paper/2005.02205/) (2020)
- [Graph Unlearning](https://mlsanity.com/paper/2103.14991/) (2021)
- [Zero-Shot Machine Unlearning](https://mlsanity.com/paper/2201.05629/) (2022)
- [Machine Unlearning for Random Forests](https://mlsanity.com/paper/2009.05567/) (2020)
- [Introducing LETOR 4.0 Datasets](https://mlsanity.com/paper/1306.2597/) (2013)
- [The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining](https://mlsanity.com/paper/2203.07320/) (2022)
- [Federated Unlearning via Class-Discriminative Pruning](https://mlsanity.com/paper/2110.11794/) (2021)
- [Machine Unlearning: Linear Filtration for Logit-based Classifiers](https://mlsanity.com/paper/2002.02730/) (2020)
- [Federated Unlearning: How to Efficiently Erase a Client in FL?](https://mlsanity.com/paper/2207.05521/) (2022)
- [Federated Unlearning with Knowledge Distillation](https://mlsanity.com/paper/2201.09441/) (2022)
- [Towards Probabilistic Verification of Machine Unlearning](https://mlsanity.com/paper/2003.04247/) (2020)
- [VeriFi: Towards Verifiable Federated Unlearning](https://mlsanity.com/paper/2205.12709/) (2022)
- [Federated Unlearning for On-Device Recommendation](https://mlsanity.com/paper/2210.10958/) (2022)
- [Certifiable Machine Unlearning for Linear Models](https://mlsanity.com/paper/2106.15093/) (2021)
- [Coded Machine Unlearning](https://mlsanity.com/paper/2012.15721/) (2020)
- [Membership Inference via Backdooring](https://mlsanity.com/paper/2206.04823/) (2022)
- [Differentiable Unbiased Online Learning to Rank](https://mlsanity.com/paper/1809.08415/) (2018)
- [Efficient Exploration of Gradient Space for Online Learning to Rank](https://mlsanity.com/paper/1805.07317/) (2018)
- [Variance Reduction in Gradient Exploration for Online Learning to Rank](https://mlsanity.com/paper/1906.03766/) (2019)
- [Reinforcement Online Learning to Rank with Unbiased Reward Shaping](https://mlsanity.com/paper/2201.01534/) (2022)
- [Is Non-IID Data a Threat in Federated Online Learning to Rank?](https://mlsanity.com/paper/2204.09272/) (2022)
- [An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems](https://mlsanity.com/paper/2307.01565/) (2023)

## Built on by (most cited first, 4 of 4)

- [Machine Unlearning: A Comprehensive Survey](https://mlsanity.com/paper/2405.07406/) (2024)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Unlearning for Federated Online Learning to Rank: A Reproducibility Study](https://mlsanity.com/paper/2505.12791/) (2025)
- [A Survey on Federated Unlearning: Challenges and Opportunities](https://mlsanity.com/paper/2403.02437/) (2024)

## Abstract (arXiv)

Data protection legislation like the European Union's General Data Protection Regulation (GDPR) establishes the textitright 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.