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VaultGemma: The most capable differentially private LLM
world's most capable differentially private LLM September 12, 2025 Amer Sinha, Software Engineer, and Ryan McKenna, Research Scientist, Google Research We introduce VaultGemma, the most capable model trained from scratch with differential privacy. As AI becomes more integrated into our lives, building it with privacy at its core is a critical frontier for the field.
We assumed that how well the model learns depends mostly on the "noise-batch ratio” which compares the amount of random noise we add for privacy to the size of the data groups (batches) we use for training. The final training loss of VaultGemma was remarkably close to what our equations predicted, validating our research and providing the community with a reliable roadmap for future private model development. The following people directly contributed to the work presented here (ordered alphabetically): Borja Balle, Zachary Charles, Christopher A. Choquette-Choo, Lynn Chua, Prem Eruvbetine, Badih Ghazi, Steve He, Yangsibo Huang, Armand Joulin, George Kaissis, Pritish Kamath, Ravi Kumar, Daogao Liu, Ruibo Liu, Pasin Manurangsi, Thomas Mesnard, Andreas Terzis, Tris Warkentin, Da Yu, and Chiyuan Zhang.
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