In this paper, we provide experimental evidence for the benefits of multi-task learning in the context of masked AES implementations (via the ASCADv1-r and ASCADv2 databases). We develop an approach for comparing single-task and multi-task approaches rather than comparing specific resulting models: we do this by training many models with random hyperparameters (instead of comparing a few highly tuned models). We find that multi-task learning has significant practical advantages that make it an attractive option in the context of device evaluations: the multi-task approach leads to performant networks quickly in particular in situations where knowledge of internal randomness is not available during training.
CITATION STYLE
Marquet, T., & Oswald, E. (2023). A Comparison of Multi-task Learning and Single-Task Learning Approaches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13907 LNCS, pp. 121–138). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-41181-6_7
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