Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917299470991360 |
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| author | Zhang, Chunxu Zhang, Weipeng Long, Guodong Xue, Zhiheng Xia, Riting Yang, Bo |
| author_facet | Zhang, Chunxu Zhang, Weipeng Long, Guodong Xue, Zhiheng Xia, Riting Yang, Bo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19955 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach Zhang, Chunxu Zhang, Weipeng Long, Guodong Xue, Zhiheng Xia, Riting Yang, Bo Information Retrieval 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. |
| title | Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2509.19955 |