FedAugment: Table Augmentation Search over Decentralized Data Repositories
Dataset search often aims to identify joinable or unionable datasets to augment a given query table. State-of-the-art approaches rely on large language models (LLMs) to embed tables into vector representations and perform semantic similarity search. However, existing work assumes a centralized data repository with embeddings generated by a single, homogeneous pipeline. In contrast to this simplifying assumption, data repositories in the real world are decentralized across multiple data providers, each operating their own embedding pipelines. Given the rapid pace of LLM development and provider-specific fine-tuning, enforcing a standardized pipeline is unrealistic. We introduce FedAugment, a framework for table augmentation search over decentralized data repositories with heterogeneous embeddings. FedAugment constructs a representative set of training examples, embeds it using the individual providers’ pipelines, and learns projection functions that align heterogeneous embeddings into a shared vector space via multi-view contrastive learning. Using these projections, all embeddings are mapped into a globally aligned space that supports unified vector similarity search. Compared to issuing independent top-𝑘 queries to each data provider, FedAugment enables the retrieval of a global top-𝑘 result across all repositories, avoiding redundant retrievals and enabling cost-efficient table augmentation search in decentralized settings.
