FedAugment: Table Augmentation Search over Decentralized Data Repositories
Summary: FedAugment enables table augmentation search across decentralized repositories with heterogeneous, provider-specific LLM embeddings. Multi-view contrastive learning aligns embeddings via learned projections, supporting globally ranked top-k retrieval without redundant per-provider queries. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Lennart Behme (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 2. Emil Badura (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 3. Leonard Geißler (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 4. Matthias Boehm (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 5. Ziawasch Abedjan (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 6. Volker Markl (Berlin Institute for the Foundations of Learning and Data; German National Research Center for Information Technology; Technical University of Berlin)
BibTeX Citation
@article{behme_vldb26,
title = {{FedAugment: Table Augmentation Search over Decentralized Data Repositories}},
author = {Behme, Lennart and Badura, Emil and Geißler, Leonard and Boehm, Matthias and Abedjan, Ziawasch and Markl, Volker},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {10},
pages = {2672--2685},
doi = {10.14778/3828612.3828623},
url = {https://doi.org/10.14778/3828612.3828623},
year = {2026}
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