arXiv:2605.24879v1 Announce Type: new Abstract: Large language models (LLMs) are trained on vast datasets that may contain sensitive information. Differential privacy (DP), the de facto standard for formal privacy guarantees, provides a principled framework for training LLMs with provable privacy protection. However, state-of-the-art DP training implementations rely on fast gradient clipping techniques with memory overhead $O(B \min\{T^2, d^2\})$, where $B$ is the batch size, $T$ is the sequence length, and $d$ is the model width. This becomes prohibitive as both model size and context length
The increasing scale of LLMs and growing public and regulatory concern over data privacy have made efficient differentially private training a critical research area.
This development addresses a fundamental technical bottleneck in applying robust privacy guarantees to large-scale AI models, potentially accelerating their adoption in sensitive applications.
The ability to efficiently apply differential privacy to LLMs at scale makes it more feasible for organizations handling sensitive data to leverage advanced AI without compromising user privacy.
- · AI developers
- · Healthcare sector
- · Financial services
- · Privacy-focused organizations
- · Organizations with poor data governance
- · Open-source LLMs without privacy controls
More widespread deployment of private LLMs across regulated industries becomes technically viable.
Increased trust in AI systems handling personal data, potentially leading to broader societal acceptance and adoption.
New regulatory frameworks may emerge, mandating differential privacy or similar techniques for AI models trained on sensitive information.
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Read at arXiv cs.LG