Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms

A new theoretical convergence analysis has been introduced for machine learning training under fully homomorphic encryption (FHE), integrating a differentially private training algorithm designed for encrypted computation. This approach enhances computational efficiency compared to traditional differentially private gradient descent methods while maintaining utility.

The significance of this development lies in its potential to facilitate scalable encrypted learning, preserving privacy in downstream tasks without the need for expensive per-sample gradient clipping.

This advancement reflects a growing emphasis on privacy-preserving techniques in machine learning, as researchers explore various methods to ensure fairness and efficiency in models, addressing challenges such as interpretability and the balance between privacy and accuracy.