A General Framework to Evaluate Robustness of Aggregation Algorithms in Federated Learning

Motivation

This repository provides a code to investigate the security of a given federated learning algorithm, and especially the security provided by robust aggregation algorithms that are used to aggregate the client updates in federated learning. The code can be used to reproduce results and conclusions from two of our works: [1] Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defensesfor Federated Learning and [2] Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning.

Our attacks are untargeted poisoning attacks, i.e., they aim to reduce the accuracy of the global model on all of the test inputs. For a detailed review of different types of attacks please refer to our thorough review paper 2.

Result Highlights

Understanding the code and using the notebooks

We have given the code in the form of notebooks which are self-explanatory, because the description of each cell is given in the respective notebooks. To run the code and reproduce the results in the paper, please clone/download the repo and simply run the notebooks, preferrably on a GPU. Various evaluation dimensions are below

• Datasets included are CIFAR10 (covers iid and cross-silo FL cases) and FEMNIST (covers non-iid and cross-device FL cases).
• We have given codes for five state-of-the-art aggregation algorithms, which give theoretical convergence guarantees: Krum, Multi-krum, Bulyan, Trimmed-mean, Median
• Baseline model poisoning attacks Fang and LIE.
• Our state-of-the-art model poisoning attacks, Aggregation-tailored attacks and Aggregation-agnsotic attacks, for the above mentioned aggregation algorithms. For any other aggregation algorithms, the code allows for simple plug-and-attack framework.

Citation

@inproceedings{shejwalkar2021manipulating,
  title={Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning},
  author={Shejwalkar, Virat and Houmansadr, Amir},
  booktitle={NDSS},
  year={2021}
}

@inproceedings{shejwalkar2022back,
  title={Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning},
  author={Shejwalkar, Virat and Houmansadr, Amir and Kairouz, Peter and Ramage, Daniel},
  booktitle={2022 IEEE Symposium on Security and Privacy (SP)},
  pages={1354--1371},
  year={2022},
  organization={IEEE}
}