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Optimized layerwise approximation for efficient private inference on fully homomorphic encryption
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DOI：10.1016/j.neucom.2026.134016.png)
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Abstract
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• Fully homomorphic encryption strongly protects sensitive data. • Fully homomorphic encryption requires approximations for non-arithmetic operations. • Post-training approximation eliminates the need for retraining the pre-trained model. • Evaluating high-degree polynomials on ciphertexts incurs significant time latency. • Optimized layerwise approximation reduces time latency for private inference.
Keywords:
Fully homomorphic encryption
Private inference
Layerwise approximation
Time latency
Non-arithmetic operations
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