Multimodal federated learning enhances model performance by integrating data from different modalities. However, this integration also introduces new privacy leakage risk associated with cross-modal correlation. Existing differential privacy methods are primarily designed for single-modal data, making them ineffective in multimodal scenarios, while noise addition often compromises data utility. In this paper, we propose an Embedding Multimodal Fusion Federated Learning Approach, called EM2FL. This approach leverages horizontal multimodal federated learning to collaboratively train global models across multiple data platforms without exposing raw private data. Instead of applying differential privacy to each modality-specific encoder separately, we implement it on the mixed embedding module that combines multiple modalities. We tested our approach on 3 distinct models and validated its effectiveness for the combination of image and user-item pair modalities. Experimental results show that our method improves performance by 10-20% compared to traditional differential privacy methods while maintaining a similar level of privacy protection. This demonstrates that EM2FL effectively balances privacy and performance through selective noise addition.

Federated learning, as an emerging distributed machine learning paradigm, allows multiple parties to jointly train models without sharing raw data, thus solving the data silo problem and enhancing data privacy protection. As a powerful tool for processing graph-structured data, graph neural networks (GNNs) have shown great potential in many fields. Federated graph neural networks (FGNNs) combine the advantages of both, allowing data from different institutions to remain localized while utilizing GNNs to model complex graph-structured data, promoting multi-party collaborative training without directly exchanging data. However, the application of FGNNs in distributed environments faces many challenges, especially in protecting data privacy. This survey aims to comprehensively explore the privacy protection technology of FGNNs, covering the privacy protection mechanism of nodes, edges, and models, while also discussing the benefits and drawbacks of each approach. Finally, the future research directions of privacy protection technology in federated graph neural networks are discussed.