FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement

Deep Cross-Modal Hashing (CMH) has become one of the most popular solutions for cross-modal retrieval. Existing methods need to first collect data and then be trained with these accumulated data. However, in real world, data may be generated and possessed by different owners. Considering the concerns about privacy, data may not be shared or transmitted, leading to the failure of sufficient training of CMH. To solve the problem, we propose a new framework called Federated Cross-modal Hashing with Adaptive Feature Enhancement (FedCAFE). FedCAFE is a federated method which could use distributed data to train existing CMH methods under the privacy protection. To overcome the data heterogeneity challenge of distributed data and improve the generalization ability of global model, FedCAFE is endowed with a novel adaptive feature enhancement module and a new weighted aggregation strategy. Besides, it could fully utilize the rich global information carried in the global model to constrain the model during the local training process. We have conducted extensive experiments on four widely-used datasets in CMH domain with both IID and non-IID settings. The reported results demonstrate that the proposed FedCAFE achieves better performance than several state-of-the-art baselines.