Towards Quantum-Safe Federated Learning via Homomorphic Encryption: Learning with Gradients
CoRR(2024)
摘要
This paper introduces a privacy-preserving distributed learning framework via
private-key homomorphic encryption. Thanks to the randomness of the
quantization of gradients, our learning with error (LWE) based encryption can
eliminate the error terms, thus avoiding the issue of error expansion in
conventional LWE-based homomorphic encryption. The proposed system allows a
large number of learning participants to engage in neural network-based deep
learning collaboratively over an honest-but-curious server, while ensuring the
cryptographic security of participants' uploaded gradients.
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