Agree on the Model, Verify the Inference: GKR Protocols for HND-Based Transformer Inference
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Abstract (arXiv)
Outsourced Transformer inference exposes clients to model substitution and incomplete execution, while direct replay removes the computational benefit of delegation. We present GKR-HND, a registered-model protocol for verifying the polynomial backbone of Homomorphic--Nonhomomorphic Decomposition Transformers. The retained verifier checks the GKR transcript and registered-weight openings, but delegates expensive public evaluations to an assigned computation worker. Assuming an honest retained verifier and prover--worker non-collusion, the verifier accepts only when the worker's signed, request-bound response agrees with the proof claims. Experiments with pretrained HND models validate the proof path and the delegated public computation without dense-matrix replay.
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References · 14
- 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,581
- 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
- 2020Reformer: The Efficient Transformercited by 1,658
- 2020Rethinking Attention with Performerscited by 1,584
- 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
- 2020Linformer: Self-Attention with Linear Complexitycited by 1,462
- 2016Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activationscited by 413
- 2024A Review on Edge Large Language Models: Design, Execution, and Applicationscited by 98
- 2024zkLLM: Zero Knowledge Proofs for Large Language Modelscited by 51
- 2017SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloudcited by 50
- 2019VeriML: Enabling Integrity Assurances and Fair Payments for Machine Learning as a Servicecited by 26
- 2013Time-Optimal Interactive Proofs for Circuit Evaluationcited by 14
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