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arXiv · 2410.08478

Federated Vision-Language-Recommendation with Personalized Fusion

Zhiwei Li·Guodong Long·Jing Jiang·Chengqi Zhang·Qiang Yang
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Abstract

Applying large pre-trained Vision-Language Models to recommendation is a burgeoning field, a direction we term Vision-Language-Recommendation (VLR). Bringing VLR to user-oriented on-device intelligence within a federated learning framework is a crucial step for enhancing user privacy and delivering personalized experiences. This paper introduces FedVLR, a federated VLR framework specially designed for user-specific personalized fusion of vision-language representations. At its core is a novel bi-level fusion mechanism: The server-side multi-view fusion module first generates a diverse set of pre-fused multimodal views. Subsequently, each client employs a user-specific mixture-of-expert mechanism to adaptively integrate these views based on individual user interaction history. This designed lightweight personalized fusion module provides an efficient solution to implement a federated VLR system. The effectiveness of our proposed FedVLR has been validated on seven benchmark datasets.

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Zhiwei Li, Guodong Long, Jing Jiang, Chengqi Zhang, Qiang Yang. 2024-10-11. Federated Vision-Language-Recommendation with Personalized Fusion. https://arxiv.org/abs/2410.08478

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