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Abstract
Federated learning (FL) has become one of the standard approaches for
deploying machine learning models on edge devices, where private training data
are distributed across clients, and a shared model is learned by aggregating
locally computed updates from each client. While this paradigm enhances
communication efficiency by only requiring updates at the end of each training
epoch, the transmitted model updates remain vulnerable to malicious tampering,
posing risks to the integrity of the global model. Although current digital
signature algorithms can protect these communicated model updates, they fail to
ensure quantum security in the era of large-scale quantum computing.
Fortunately, various post-quantum cryptography algorithms have been developed
to address this vulnerability, especially the three NIST-standardized
algorithms - Dilithium, FALCON, and SPHINCS+. In this work, we empirically
investigate the impact of these three NIST-standardized PQC algorithms for
digital signatures within the FL procedure, covering a wide range of models,
tasks, and FL settings. Our results indicate that Dilithium stands out as the
most efficient PQC algorithm for digital signature in federated learning.
Additionally, we offer an in-depth discussion of the implications of our
findings and potential directions for future research.