Multimodal Federated Learning for Personalized Clothing Recommendation

Federated clothing recommendation suggests clothing items to customers (users) based on their past purchase behaviors in a privacy-preserving manner. Current federated recommendation systems face two main challenges. The first is limited information exploration. Existing methods mainly rely on item ID-based embedding and ignore multimodal information about items. The second challenge, data heterogeneity, arising from that users perceive the same clothing item differently, leads to performance degradation. We therefore propose a multimodal federated learning framework for personalized clothing recommendation (MMFashion). To address the first challenge, we represent each item using its image and text descriptions, generating rich item representations from which user preferences can be derived. To address the second challenge, we introduce personalized image and text adapters to guide the local model in capturing item attributes that align with user’s interests. Such user-specific item representations effectively capture individual preferences. We also introduce a threshold-based regularization loss to push the learned embeddings of user preferences and dislikes apart. To evaluate the effectiveness of MMFashion, we construct datasets by extracting two clothing types (i.e., long sleeves and outerwear) from a real-world clothing dataset released by a fashion retail company. Experimental results show that MMFashion consistently outperforms existing methods, supporting its effectiveness in addressing the challenges in federated clothing recommendation.

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