FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs. (arXiv:2306.04959v1 [cs.CR])

This paper introduces FedMLSecurity, a benchmark that simulates adversarial
attacks and corresponding defense mechanisms in Federated Learning (FL). As an
integral module of the open-sourced library FedML that facilitates FL algorithm
development and performance comparison, FedMLSecurity enhances the security
assessment capacity of FedML. FedMLSecurity comprises two principal components:
FedMLAttacker, which simulates attacks injected into FL training, and
FedMLDefender, which emulates defensive strategies designed to mitigate the
impacts of the attacks. FedMLSecurity is open-sourced 1 and is customizable to
a wide …

adversarial adversarial attacks algorithm assessment attacks benchmark defense development federated learning library llms performance security security assessment