Implementation for NeurIPS 2021 Submission: SparseFed

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Deep Learning sparsefed
Overview

READ THIS FIRST

This repo is an anonymized version of an existing repository of GitHub, for the AIStats 2021 submission: SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. So if you see another repo that looks identical to this, we are not stealing anyone's code, that's my repo.

SparseFed

This repo contains an implementation of model poisoning attacks on a federated learning system.

It comes with a few experimental setups; various Residual Networks on CIFAR10, CIFAR100, FEMNIST, ImageNet (cv_train.py) and GPT2 on PersonaChat (gpt2_train.py) (attack is currently not implemented for PersonaChat)

There are a variety of command-line args which are best examined by looking at utils.py

The server is contained in fed_aggregator.py and the worker is contained in fed_worker.py

To use sketching, you need to install https://github.com/nikitaivkin/csh

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Comments
  • CVE-2007-4559 Patch

    CVE-2007-4559 Patch

    Patching CVE-2007-4559

    Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

    If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

    opened by TrellixVulnTeam 0
Owner
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