Can We Trust the Similarity Measurement in Federated Learning?
Zhilin Wang Qin Hu Xukai Zou
Is it secure to measure the reliability of local models by similarity in
federated learning (FL)? This paper delves into an unexplored security threat
concerning applying similarity metrics, such as the L_2 norm, Euclidean
distance, and cosine similarity, in protecting FL. We first uncover the
deficiencies of similarity metrics that high-dimensional local models,
including benign and poisoned models, may be evaluated to have the same
similarity while being significantly different in the parameter values. We then
leverage this finding to devise a novel untargeted model poisoning attack,
Faker, which launches the attack by simultaneously maximizing the evaluated
similarity of the poisoned local model and the difference in the parameter
values. Experimental results based on seven datasets and eight defenses show
that Faker outperforms the state-of-the-art benchmark attacks by 1.1-9.0X in
reducing accuracy and 1.2-8.0X in saving time cost, which even holds for the
case of a single malicious client with limited knowledge about the FL system.
Moreover, Faker can degrade the performance of the global model by attacking
only once. We also preliminarily explore extending Faker to other attacks, such
as backdoor attacks and Sybil attacks. Lastly, we provide a model evaluation
strategy, called the similarity of partial parameters (SPP), to defend against
Faker. Given that numerous mechanisms in FL utilize similarity metrics to
assess local models, this work suggests that we should be vigilant regarding
the potential risks of using these metrics.