SecMoE: Communication-Efficient Secure MoE Inference via Select-Then-Compute
Bowen Shen, Yuyue Chen, Peng Yang, Bin Zhang, Xi Zhang, Zoe L. Jiang
Abstract
Privacy-preserving Transformer inference has gained attention due to the potential leakage of private information. Despite recent progress, existing frameworks still fall short of practical model scales, with gaps up to a hundredfold. A possible way to close this gap is the Mixture of Experts (MoE) architecture, which has emerged as a promising technique to scale up model capacity with minimal overhead. However, given that the current secure two-party (2-PC) protocols allow the server to homomorphically compute the FFN layer with its plaintext model weight, under the MoE setting, this could reveal which expert is activated to the server, exposing token-level privacy about the client
BibTeX
@inproceedings{aaai2026_secmoecommunicat,
title = {SecMoE: Communication-Efficient Secure MoE Inference via Select-Then-Compute},
author = {Bowen Shen and Yuyue Chen and Peng Yang and Bin Zhang and Xi Zhang and Zoe L. Jiang},
booktitle = {AAAI 2026},
year = {2026}
}