UniCon: Unidirectional Split Learning with Contrastive Loss for Visual Question Answering

Visual question answering (VQA) that leverages multi-modality data has attracted intensive interest in real-life applications, such as home robots and clinic diagnoses. Nevertheless, one of the challenges is to design robust learning for different client tasks. This work aims to bridge the gap between the prerequisite of large-scale training data and the constraint of client data sharing mainly due to confidentiality. We propose the Unidirectional Split Learning with Contrastive Loss (UniCon) to tackle VQA tasks training on distributed data silos. In particular, UniCon trains a global model over the entire data distribution of different clients learning refined cross-modal representations via contrastive learning. The learned representations of the global model aggregate knowledge from different local tasks. Moreover, we devise a unidirectional split learning framework to enable more efficient knowledge sharing. The comprehensive experiments with five state-of-the-art VQA models on the VQA-v2 dataset demonstrated the efficacy of UniCon, achieving an accuracy of 49.89% in the validation set of VQA-v2. This work is the first study of VQA under the constraint of data confidentiality using self-supervised Split Learning.