# FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients

arXiv: https://arxiv.org/abs/2405.02881 · HTML: https://mlsanity.com/paper/2405.02881/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 5 May 2024.

## Builds on (most cited first, 13 of 13)

- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [Federated Linear Contextual Bandits](https://mlsanity.com/paper/2110.14177/) (2021)
- [Conversational Contextual Bandit: Algorithm and Application](https://mlsanity.com/paper/1906.01219/) (2019)
- [Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits](https://mlsanity.com/paper/2110.01463/) (2021)
- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (2022)
- [On Differentially Private Federated Linear Contextual Bandits](https://mlsanity.com/paper/2302.13945/) (2023)
- [Comparison-based Conversational Recommender System with Relative Bandit Feedback](https://mlsanity.com/paper/2208.09837/) (2022)
- [Efficient Explorative Key-term Selection Strategies for Conversational Contextual Bandits](https://mlsanity.com/paper/2303.00315/) (2023)
- [Federated Online Clustering of Bandits](https://mlsanity.com/paper/2208.14865/) (2022)
- [Hierarchical Conversational Preference Elicitation with Bandit Feedback](https://mlsanity.com/paper/2209.06129/) (2022)
- [On-Demand Communication for Asynchronous Multi-Agent Bandits](https://mlsanity.com/paper/2302.07446/) (2023)
- [Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users](https://mlsanity.com/paper/2402.16312/) (2024)

## Built on by (most cited first, 6 of 6)

- [A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses](https://mlsanity.com/paper/2501.01849/) (2025)
- [Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts](https://mlsanity.com/paper/2501.00891/) (2025)
- [Federated Linear Dueling Bandits](https://mlsanity.com/paper/2502.01085/) (2025)
- [Leveraging the Power of Conversations: Optimal Key Term Selection in Conversational Contextual Bandits](https://mlsanity.com/paper/2505.21393/) (2025)
- [Fusing Reward and Dueling Feedback in Stochastic Bandits](https://mlsanity.com/paper/2504.15812/) (2025)
- [Trading Vector Data in Vector Databases](https://mlsanity.com/paper/2511.07139/) (2025)

## Abstract (arXiv)

Conversational recommender systems have emerged as a potent solution for efficiently eliciting user preferences. These systems interactively present queries associated with "key terms" to users and leverage user feedback to estimate user preferences more efficiently. Nonetheless, most existing algorithms adopt a centralized approach. In this paper, we introduce FedConPE, a phase elimination-based federated conversational bandit algorithm, where M agents collaboratively solve a global contextual linear bandit problem with the help of a central server while ensuring secure data management. To effectively coordinate all the clients and aggregate their collected data, FedConPE uses an adaptive approach to construct key terms that minimize uncertainty across all dimensions in the feature space. Furthermore, compared with existing federated linear bandit algorithms, FedConPE offers improved computational and communication efficiency as well as enhanced privacy protections. Our theoretical analysis shows that FedConPE is minimax near-optimal in terms of cumulative regret. We also establish upper bounds for communication costs and conversation frequency. Comprehensive evaluations demonstrate that FedConPE outperforms existing conversational bandit algorithms while using fewer conversations.