Decentralized and Collaborative AI on Blockchain

Abstract

Machine learning has recently enabled large advances in artificial intelligence, but these tend to be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and re-train them. We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model. This model will be shared publicly on a blockchain where it can be free to use for inference. Ideal learning problems include scenarios where a model is used many times for similar input such as personal assistants, playing games, recommender systems, etc. In order to maintain the model's accuracy with respect to some test set we propose both financial and non-financial (gamified) incentive structures for providing good data. A free and open source implementation for the Ethereum blockchain is provided at https://github.com/microsoft/0xDeCA10B.

Analysis of Models for Decentralized and Collaborative AI on Blockchain

“…That work focuses on the description of several possible incentive mechanisms to encourage participants to add data to train a model. This is the continuation of previous work in [3] by the author.…”

“…In this section, we outline several models choices of machine learning model for use with Decentralized and Collaborative AI on Blockchain as proposed in [3]. The model architecture chosen relates closely to the incentive mechanism chosen.…”

“…This should even give them the assurance to buy the first version of a product and knowing that it should improve without them getting forced into buying a later version of the product. Even if the model does get corrupted, applications can easily revert to an earlier version on the blockchain, still giving users the service they need [3].…”

“…To address centralization in machine learning, frameworks to share machine learning models on a public blockchain while keeping the models free to use for inference have been proposed. One example is Decentralized and Collaborative AI on Blockchain from Microsoft Research [3]. That work focuses on the description of several possible incentive mechanisms to encourage participants to add data to train a model.…”

“…The Self-Assessment IM allows ongoing verification of data contributions without the need for a centralized party to evaluate data contributions. Here are the highlights of the IM as explained in [3]:…”