🧵 Researchers Release ZK-EdgeLoRA for LoRA Verification In Brief: Southeast University and Singapore Management University have released ZK-EdgeLoRA, which is derived from @bagelopenai's zkLoRA. It uses zero-knowledge proofs to verify LoRA adapters for LLMs in edge environments. 1/ New Protocol Researchers from Southeast University and Singapore Management University have released ZK-EdgeLoRA. It extends zkLoRA and applies zero-knowledge proofs to verify LoRA adapters for LLMs in trustless distributed edge environments. 2/ Importance LoRA adapters can be used to adapt LLMs to specific domains at a low cost, but they increase risk when shared by external contributors. ZK-EdgeLoRA confirms that adapters match the underlying model and are calculated correctly without exposing weights. 3/ Methodology The protocol uses a VOLE-based commit-and-prove method to batch-verify matrix operations. This method enables fast verification of adapter computations and protects data privacy in distributed environments. 4/ Performance When tested using HuggingFace's medical LoRA adapter, ZK-EdgeLoRA confirmed correctness within 0.1 to 2.8 seconds, depending on the adapter size. These results demonstrate that this approach is highly efficient for real-world and time-sensitive applications. 5/ Broader Context ZK-EdgeLoRA, a derivative of Bagel Open AI's zkLoRA, privately verifies computations and establishes a trust framework for modular LLMs. It enables distributed deployments that balance efficiency and security in an open environment.