Towards Quantum-Safe Distributed Learning via Homomorphic Encryption: Learning with Gradients

This paper introduces a privacy-preserving distributed learning framework via private-key homomorphic encryption. Using randomness in the quantization of gradients, our encryption replaces the Gaussian error term of Learning With Errors (LWE) with quantized gradients, thus reducing the error expansion speed in conventional LWE-based homomorphic en-cryption. The proposed system allows a large number of learning participants to engage in distributed learning collaboratively over an honest-but-curious server, while ensuring the cryptographic security of participants' uploaded gradients.