Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach

• URL: http://arxiv.org/abs/2509.19955v1
• Date: Wed, 24 Sep 2025 10:06:37 GMT
• Title: Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach
• Authors: Chunxu Zhang, Weipeng Zhang, Guodong Long, Zhiheng Xue, Riting Xia, Bo Yang,
• Abstract summary: Federated Recommendation (FR) is a new learning paradigm to tackle the learn-to-rank problem in a privacy-preservation manner.<n>We propose a novel multimodal fusion mechanism in federated recommendation settings (GFMFR)<n>It offloads multimodal representation learning to the server, which stores item content and employs a high-capacity encoder to generate expressive representations.
• Score: 29.530957799669398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Recommendation (FR) is a new learning paradigm to tackle the learn-to-rank problem in a privacy-preservation manner. How to integrate multi-modality features into federated recommendation is still an open challenge in terms of efficiency, distribution heterogeneity, and fine-grained alignment. To address these challenges, we propose a novel multimodal fusion mechanism in federated recommendation settings (GFMFR). Specifically, it offloads multimodal representation learning to the server, which stores item content and employs a high-capacity encoder to generate expressive representations, alleviating client-side overhead. Moreover, a group-aware item representation fusion approach enables fine-grained knowledge sharing among similar users while retaining individual preferences. The proposed fusion loss could be simply plugged into any existing federated recommender systems empowering their capability by adding multi-modality features. Extensive experiments on five public benchmark datasets demonstrate that GFMFR consistently outperforms state-of-the-art multimodal FR baselines.

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