A Federated Framework for LLM-based Recommendation

TL;DR

This paper introduces FELLRec, a federated learning framework for LLM-based recommendation systems that enhances privacy, balances client performance, and reduces resource costs through dynamic and flexible strategies.

Contribution

FELLRec is a novel federated framework that addresses client performance imbalance and resource costs in LLM-based recommendation systems by employing dynamic parameter aggregation and selective layer offloading.

Findings

FELLRec achieves more balanced client performance.

It improves overall recommendation accuracy.

It reduces computational and storage costs.

Abstract

Large Language Models (LLMs) have empowered generative recommendation systems through fine-tuning user behavior data. However, utilizing the user data may pose significant privacy risks, potentially leading to ethical dilemmas and violations of data protection regulations. To address the privacy concerns, Federated Learning for Recommendation (Fed4Rec) has been identified as a promising solution. However, directly applying Fed4Rec in the LLM context introduces two challenges: 1) exacerbated client performance imbalance, which ultimately impacts the system's long-term effectiveness, and 2) substantial client resource costs, posing a high demand for clients' both computational and storage capability to locally train and infer LLMs. To tackle these challenges, we propose a federated framework for LLM-based recommendation (shorted as FELLRec). Generally, FELLRec designs two key strategies. 1) Dynamic balance strategy, which designs dynamic parameter aggregation and learning speed for different clients, aiming to ensure balanced performance across clients. 2) Flexible storage strategy, which selectively retains certain sensitive LLM layers on the client side, while offloading other layers to the server, aiming to preserve privacy while saving resources. Experiment results show that FELLRec can achieve a more balanced client performance and improved overall performance in a computational and storage-efficient way while safeguarding user privacy well.