Towards Resource-Efficient and Secure Federated Multimedia Recommendation

Abstract

Federated multimedia recommendation remains unexplored due to the high dimensionality of multimedia context, which limits the federated optimization on resource-constrained user devices. To address this issue, we propose a resource-efficient and secure federated learning framework for multimedia recommendation. Instead of training the entire model, we split the multimodal learning model to the powerful server, and the client trains the lightweight collaborative filtering model. Only the local model and item representations are transferred between the server and clients. We also propose an inter-client convolution strategy that utilizes secure multi-party computation to guarantee user privacy while alleviating heterogeneity among clients. We conduct evaluations on three datasets and demonstrate that our proposed method effectively exploits the modality features of items to improve performance while significantly reducing the communication and computation cost for clients.