PredictChain: Empowering Collaboration and Data Accessibility for AI in
  a Decentralized Blockchain-based Marketplace
Matthew T. Pisano Connor J. Patterson Oshani Seneviratne
Limited access to computing resources and training data poses significant
challenges for individuals and groups aiming to train and utilize predictive
machine learning models. Although numerous publicly available machine learning
models exist, they are often unhosted, necessitating end-users to establish
their computational infrastructure. Alternatively, these models may only be
accessible through paid cloud-based mechanisms, which can prove costly for
general public utilization. Moreover, model and data providers require a more
streamlined approach to track resource usage and capitalize on subsequent usage
by others, both financially and otherwise. An effective mechanism is also
lacking to contribute high-quality data for improving model performance. We
propose a blockchain-based marketplace called "PredictChain" for predictive
machine-learning models to address these issues. This marketplace enables users
to upload datasets for training predictive machine learning models, request
model training on previously uploaded datasets, or submit queries to trained
models. Nodes within the blockchain network, equipped with available computing
resources, will operate these models, offering a range of archetype machine
learning models with varying characteristics, such as cost, speed, simplicity,
power, and cost-effectiveness. This decentralized approach empowers users to
develop improved models accessible to the public, promotes data sharing, and
reduces reliance on centralized cloud providers.