Dropbear: Machine Learning Marketplaces made Trustworthy with Byzantine Model Agreement
Alex Shamis, Peter Pietzuch, Antoine Delignat-Lavaud, Andrew Paverd, Manuel CostaPublished in ArXiv, 2022
We describe Dropbear, the first ML model marketplace that provides clients with strong integrity guarantees by combining results from multiple models in a trustworthy fashion. Dropbear replicates inference computation across a model group, which consists of multiple cloud-based GPU nodes belonging to different model owners. Clients receive inference certificates that prove agreement using a Byzantine consensus protocol, even under model heterogeneity and concurrent model updates. To improve performance, Dropbear batches inference and consensus operations separately: it first performs the inference computation across a model group, before ordering requests and model updates. Despite its strong integrity guarantees, Dropbears performance matches that of state-of-the-art ML inference systems: deployed across 3 cloud sites, it handles 800 requests/s with ImageNet models.
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