Dropbear: Machine Learning Marketplaces made Trustworthy with Byzantine Model Agreement
Alex Shamis, Peter Pietzuch, Antoine Delignat-Lavaud, Andrew Paverd, Manuel Costa
Published in ArXiv, 2022
Marketplaces for machine learning (ML) models are emerging as a way for organizations to monetize models. They allow model owners to retain control over hosted models by using cloud resources to execute ML inference requests for a fee, preserving model confidentiality. Clients that rely on hosted models require trustworthy inference results, even when models are managed by third parties. While the resilience and robustness of inference results can be improved by combining multiple independent models, such support is unavailable in today’s marketplaces.
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.
