Dimitrios Sygletos
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126
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Crypto×1
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2026
Kernel-Based ReLU Approximation for Homomorphic Encryption-Compatible Privacy-preserving Deep Learning Models
The paper proposes a kernel-based, polynomial approximation of the ReLU activation function to enable the use of non-linear deep learning models, such as LLMs, within the constraints of Homomorphic Encryption (HE) for privacy-preserving computation.
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