A blockchain empowered federated differentiable search index framework for secure information collaboration

Efficient and reliable information collaboration is essential for prompt response and effective decision-making in emergency management. Current solutions face significant challenges in cross-domain data retrieval and sharing, including data fragmentation, lack of unified indexing mechanisms, and insufficient privacy protection. This paper proposes a Federated Differentiable Search Index (FeDSI) framework to address these challenges., FeDSI integrates generative retrieval, federated learning, and blockchain technology into a unified architecture to support secure, decentralized, and privacy-preserving data collaboration. The core innovation lies in adopting a differentiable search index—a learnable document indexing mechanism optimized via backpropagation—which enables semantic-based document identification directly from user queries. The model is collaboratively trained across multiple organizations using federated learning, with blockchain smart contracts ensuring transparency and verifiability of the training and retrieval processes. Experimental results on the NQ320K benchmark show that FeDSI outperforms classical sparse and dense retrieval baselines, and achieves competitive performance compared to state-of-the-art generative retrieval models, achieving a Recall@10 of 85.40 and an MRR@100 of 72.69. These findings demonstrate the effectiveness of FeDSI in supporting secure, efficient, and collaborative information retrieval in complex emergency data environments.