FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs
CoRR(2023)
摘要
This paper introduces FedSecurity, an end-to-end benchmark designed to
simulate adversarial attacks and corresponding defense mechanisms in Federated
Learning (FL). FedSecurity comprises two pivotal components: FedAttacker, which
facilitates the simulation of a variety of attacks during FL training, and
FedDefender, which implements defensive mechanisms to counteract these attacks.
As an open-source library, FedSecurity enhances its usability compared to
from-scratch implementations that focus on specific attack/defense scenarios
based on the following features: i) It offers extensive customization options
to accommodate a broad range of machine learning models (e.g., Logistic
Regression, ResNet, and GAN) and FL optimizers (e.g., FedAVG, FedOPT, and
FedNOVA); ii) it enables exploring the variability in the effectiveness of
attacks and defenses across different datasets and models; and iii) it supports
flexible configuration and customization through a configuration file and some
provided APIs. We further demonstrate FedSecurity's utility and adaptability
through federated training of Large Language Models (LLMs), showcasing its
potential to impact a wide range of complex applications.
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关键词
federated
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