论文标题
MPAF：基于虚假客户的联邦学习的模型中毒攻击
MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients
论文作者
论文摘要
对联邦学习的现有模型中毒攻击假设攻击者可以访问大量妥协的真实客户。但是，这种假设在涉及数百万客户的联合学习系统中并不现实。在这项工作中，我们提出了基于名为MPAF的假客户的第一个模型中毒攻击。具体来说，我们假设攻击者将伪造的客户注入联合学习系统，并在培训期间将精心制作的伪造的本地模型更新发送到云服务器，以便对许多不加区分的测试输入的全局模型的准确性较低。为了实现这一目标，我们的攻击将全球模型拖到了攻击者选择的基本模型，该模型的精度较低。具体而言，在每一轮联合学习中，假客户制作了伪造的本地模型更新，该模型指向基本模型并扩展它们以扩大其影响，然后再将其发送到云服务器。我们的实验表明，即使采用了经典的防御和规范剪裁，MPAF也可以显着降低全球模型的测试准确性，这突出了对更高级防御的需求。
Existing model poisoning attacks to federated learning assume that an attacker has access to a large fraction of compromised genuine clients. However, such assumption is not realistic in production federated learning systems that involve millions of clients. In this work, we propose the first Model Poisoning Attack based on Fake clients called MPAF. Specifically, we assume the attacker injects fake clients to a federated learning system and sends carefully crafted fake local model updates to the cloud server during training, such that the learnt global model has low accuracy for many indiscriminate test inputs. Towards this goal, our attack drags the global model towards an attacker-chosen base model that has low accuracy. Specifically, in each round of federated learning, the fake clients craft fake local model updates that point to the base model and scale them up to amplify their impact before sending them to the cloud server. Our experiments show that MPAF can significantly decrease the test accuracy of the global model, even if classical defenses and norm clipping are adopted, highlighting the need for more advanced defenses.
