Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
arXiv:2605.27782v1 Announce Type: new Abstract: We present the first theoretical convergence analysis of machine learning training under fully homomorphic encryption (FHE), combined with a differentially private (DP) training algorithm tailored to encrypted computation. Our approach improves computational efficiency over standard differentially private gradient descent (DP-GD) while achieving comparable utility. In particular, we prove convergence of approximate gradient descent using polynomial approximations of activation and loss functions, which are required for FHE compatibility. To prese
The increasing need for privacy-preserving AI combined with advances in homomorphic encryption research makes this a timely development for practical applications.
This research provides theoretical and algorithmic breakthroughs for privacy-preserving machine learning, essential for secure AI deployments across sensitive domains.
Machine learning models can now be trained with stronger privacy guarantees and provable convergence, even when data remains encrypted, opening new possibilities for sensitive data utilization.
- · Privacy-focused AI companies
- · Healthcare and finance sectors
- · Data privacy regulators
- · Homomorphic encryption developers
- · Centralized data aggregators (without privacy tech)
- · Black Box AI solutions (without transparency)
- · Cybersecurity attackers targeting data in use
More secure and compliant AI systems become feasible, accelerating adoption in regulated industries.
An ecosystem of FHE-compatible ML frameworks and tools will emerge, driving down the barriers to implementing private AI.
Enhanced trust in AI systems could lead to broader societal acceptance and integration of AI in highly sensitive applications, potentially influencing public policy on data sharing.
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Read at arXiv cs.LG