FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients

TL;DR

FedConPE is a federated conversational bandit algorithm that improves efficiency, privacy, and performance in multi-agent settings for recommender systems, with theoretical guarantees and superior empirical results.

Contribution

Introduces FedConPE, a novel federated conversational bandit algorithm with adaptive key term construction, enhancing efficiency and privacy over existing methods.

Findings

Achieves near-optimal regret bounds.

Reduces communication and conversation costs.

Outperforms existing algorithms in experiments.

Abstract

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.