EHEIR: Efficient Homomorphic Encrypted Inference via Architectural Redesign

Le résumé fourni par la source

Encrypted inference under homomorphic encryption (HE) imposes stringent constraints on computation, making the direct deployment of deep neural networks infeasible on resource-constrained edge devices. Existing approaches, such as FHEEDGE and UniHENN, primarily focus on improving the efficiency of encrypted computation through algorithmic and systems-level optimizations. In contrast, we adopt a complementary perspective by redesigning neural architectures to align with the inherent cost model of HE operations. In this work, we present a framework that reformulates HE-aware model design as a constrained neural architecture search problem, where the objective is to identify architectures that are both cryptographically feasible and computationally efficient while preserving task performance. Starting from a pretrained network, we perform a structured, iterative transformation process that includes HE-compatible operator substitutions, depth–width trade-offs, and rotation-efficient design strategies such as successive average pooling. These transformations are guided by constraints on ciphertext slots, multiplicative depth, and rotation complexity, and are evaluated using short-horizon training to efficiently explore the architecture space. We provide a theoretical analysis showing that the transformed network approximates the original model with bounded error under layer-wise Lipschitz propagation, ensuring controlled performance degradation. As a case study, we apply the proposed framework to encrypted crack detection for structural health monitoring. The optimized model achieves an execution time of 86.5 seconds for single-threaded CPU inference in the encrypted domain, compared to an estimated 3.5 hours required by the original encrypted convolutional pipeline. Furthermore, deployment on a Raspberry Pi demonstrates end-to-end encrypted inference within approximately 7.5 minutes. These results highlight that architecture-level optimization, when framed as a constrained search problem, complements existing algorithmic acceleration approaches and provides a practical pathway for enabling efficient and privacy-preserving inference on edge devices across a broad class of sensitive AI workloads.

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