Golden Grain: Building a Secure and Decentralized Model Marketplace for MLaaS
This addresses a bottleneck for MLaaS providers, especially startups, by creating a secure and incentivized model-sharing platform, though it is incremental as it builds on existing blockchain and TEE technologies.
The paper tackles the problem of insufficient incentives for individual developers to share well-trained models with MLaaS providers by building a decentralized marketplace called Golden Grain, which uses blockchain and trusted execution environments to ensure fair model-money swapping and transparent benchmarking, achieving practically affordable performance in experiments.
ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction services. To achieve this, current MLaaS systems integrate and combine multiple well-trained models in their services. Yet, in reality, there is no easy way for MLaaS providers, especially for startups, to collect sufficiently well-trained models from individual developers, due to the lack of incentives. In this paper, we aim to fill this gap by building up a model marketplace, called as Golden Grain, to facilitate model sharing, which enforces the fair model-money swapping process between individual developers and MLaaS providers. Specifically, we deploy the swapping process on the blockchain, and further introduce a blockchain-empowered model benchmarking process for transparently determining the model prices according to their authentic performances, so as to motivate the faithful contributions of well-trained models. Especially, to ease the blockchain overhead for model benchmarking, our marketplace carefully offloads the heavy computation and designs a secure off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for ensuring both the integrity and authenticity of benchmarking. We implement a prototype of our Golden Grain on the Ethereum blockchain, and conduct extensive experiments using standard benchmark datasets to demonstrate the practically affordable performance of our design.