Integrity of peer-to-peer distributed LLM inference under malicious nodes
Peer-to-peer distributed inference executes a Large Language Model (LLM) on pooled consumer hardware by spreading its layers across many nodes. Every request passes through nodes that are owned and controlled by multiple independent parties. However, in this setting, any party can tamper with the output of its layers to corrupt the end result. Recomputing the forward pass on trusted hardware can catch this, but it introduces additional computational cost. The scientific literature includes several prior integrity-checking approaches, such as known-answer traps for image classifiers and cryptographic commitments. However, these solutions test only the exact correctness and do not account f...
| Authors | Mert Cihangiroglu, Antonino Nocera |
|---|---|
| Primary category | cs.CR |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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