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Found 4 papers, 0 papers with code
FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference
We observe the overhead mainly comes from the neglect of 1) the one-hot nature of user queries and 2) the robustness of the embedding table to low bit-width quantization noise.
PrivCirNet: Efficient Private Inference via Block Circulant Transformation
Homomorphic encryption (HE)-based deep neural network (DNN) inference protects data and model privacy but suffers from significant computation overhead.
HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference
Compared with prior-art HE-based protocols, e. g., CrypTFlow2, Cheetah, Iron, etc, HEQuant achieves $3. 5\sim 23. 4\times$ communication reduction and $3. 0\sim 9. 3\times$ latency reduction.
Falcon: Accelerating Homomorphically Encrypted Convolutions for Efficient Private Mobile Network Inference
Efficient networks, e. g., MobileNetV2, EfficientNet, etc, achieves state-of-the-art (SOTA) accuracy with lightweight computation.
