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ZK-APEX

This repo provides the code to recreate the results of ZK-APEX. The base implementation is adapted from Taker.

Repo Overview

The experiment folders, specifically exp1-vit-pruning/ and exp2-llm-pruning/, can be used to duplicate the findings of the paper. The implementation of the pruning can be found in taker_mmd/src/taker/weight_pruning.py and taker_mmd/src/taker/weight_prunin_llm.

Setup

The experiments are best run with Python 3.10.

Run the following in the root directory.

$ pip install -r

and

$ pip install ./taker

Finally, access to the Imagenet-1k is required. Navigate, here and request access. Account creation may be required.

Running experiments

Before running, run export HF_TOKEN="TOKEN", with your hugging face access token. Run run-llm-pruning.sh or run-vit-pruning.sh to reproduce results. The hyperparameters can be changed in the bash files.

About

ZK-APEX: verifiable unlearning for personalized edge ML. Masked unlearning + Group-OBS compensation with zero-knowledge proofs. Reproduces ViT-B/16 and OPT-125M experiments, ablations (AdaptFormer/LoRA), and forget-set scaling/structured forgetting.

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