A Multi-Armed Bandit-Based Participant Selection Method for Federated Recommendation Systems

TL;DR

This paper introduces a Multi-Armed Bandit-based participant selection method for federated recommendation systems, enhancing training efficiency and model quality in heterogeneous edge-cloud environments.

Contribution

It formulates participant selection as a utility optimization problem and proposes a dynamic MAB-based framework that balances exploration and exploitation.

Findings

Outperforms baseline methods across eight data-skew scenarios.

Improves training efficiency by 32-50%.

Enhances model quality metrics such as Recall@50 by up to 5%.

Abstract

Federated Recommendation Systems (FRS) enable privacy-preserving model training by keeping user data on edge devices. However, the practical deployment of FRS in Edge-Cloud environments faces significant challenges due to system and statistical heterogeneity. Existing FRS participant selection strategies struggle to dynamically balance the trade-off between model convergence speed and recommendation quality in such volatile environments. To address this, we formulate the FRS participant selection problem as a normalized utility cost addressing the model quality and system efficiency. Next, we propose a dynamic participant selection framework incorporating a Multi-Armed Bandit (MAB)-based solver for multimodal FRS. We design a client-utility function that jointly evaluates historical Client Performance Reputation, data quality, and real-time system latency. By leveraging an Upper Confidence Bound strategy, our framework effectively balances the exploration of under-sampled clients with the exploitation of high-performing ones. We validate the proposed approach on a realistic edge-cloud testbed implementation using a multimodal movie-recommendation task. Experimental results demonstrate that our MAB-driven approach outperforms other baselines across eight different data-skew scenarios. Specifically, it improves training efficiency by 32-50% while improving model quality metrics such as Recall@50 by up to around 5%