Analysis of Models for Decentralized and Collaborative AI on Blockchain (2009.06756v2)

Abstract: Machine learning has recently enabled large advances in artificial intelligence, but these results can 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are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and maintain them. Published proposals to provide models and data for free for certain tasks include Microsoft Research's Decentralized and Collaborative AI on Blockchain. The framework allows participants to collaboratively build a dataset and use smart contracts to share a continuously updated model on a public blockchain. The initial proposal gave an overview of the framework omitting many details of the models used and the incentive mechanisms in real world scenarios. In this work, we evaluate the use of several models and configurations in order to propose best practices when using the Self-Assessment incentive mechanism so that models can remain accurate and well-intended participants that submit correct data have the chance to profit. We have analyzed simulations for each of three models: Perceptron, Na\"ive Bayes, and a Nearest Centroid Classifier, with three different datasets: predicting a sport with user activity from Endomondo, sentiment analysis on movie reviews from IMDB, and determining if a news article is fake. We compare several factors for each dataset when models are hosted in smart contracts on a public blockchain: their accuracy over time, balances of a good and bad user, and transaction costs (or gas) for deploying, updating, collecting refunds, and collecting rewards. A free and open source implementation for the Ethereum blockchain and simulations written in Python is provided at https://github.com/microsoft/0xDeCA10B. This version has updated gas costs using newer optimizations written after the original publication.

• The paper demonstrates that blockchain-based decentralized AI can harness self-assessment incentive mechanisms to maintain model accuracy and data integrity.
• It compares Perceptron, Naïve Bayes, and Nearest Centroid models across diverse datasets, revealing the Perceptron’s cost efficiency alongside its sensitivity to adversarial inputs.
• The study quantifies economic dynamics on Ethereum by highlighting how transaction costs influence the interactions between good and bad agents in decentralized deployments.

Analysis of Models for Decentralized and Collaborative AI on Blockchain

The paper "Analysis of Models for Decentralized and Collaborative AI on Blockchain" explores the integration of blockchain technology with decentralized machine learning models, aiming to democratize AI by enabling collaborative data and model ownership. This paper critically examines Microsoft's proposal for Decentralized and Collaborative AI on Blockchain, particularly focusing on the Self-Assessment incentive mechanism used to maintain model accuracy and data integrity.

Key Contributions

The research evaluates three models—Perceptron, Naïve Bayes, and Nearest Centroid—across three datasets: Endomondo activity data, IMDB movie reviews for sentiment analysis, and fake news detection. By deploying these models within Ethereum smart contracts, the paper investigates their accuracy, economic interactions between good and bad agents (participants), and associated transaction costs (gas fees).

Models and Implementation

1. Perceptron: A linear classifier advantageous for its low computational requirements. The Perceptron model showed variable performance across tasks, notable for its cost efficiency in blockchain deployment.
2. Naïve Bayes: This model, with its explicit probabilistic framework and simplicity, rivals others in accuracy, particularly on sentiment analysis tasks.
3. Nearest Centroid: Computes class centroids for classification, beneficial in handling changes incrementally with moderate computational demands.

Experiments and Results

The authors detail simulations observing the interactions between "good" and "bad" agents, analyzing factors such as model accuracy and agent financial outcomes. Each experiment benchmarks models against ideal baseline performances (trained on the full dataset) and measures deployment and update costs in Ethereum's ecosystem.

• Accuracy Maintenance: While all models generally upheld their accuracy, the Perceptron model notably showed sensitivity to adversarial input in some scenarios.
• Gas Costs: The Perceptron consistently required less gas than the other models. This finding is critical, as lower transaction costs promote the feasibility of deploying and maintaining decentralized AI on public blockchains.
• Economic Dynamics: Good agents were able to profit and positively influence model integrity, while bad agents could disrupt models but eventually incur financial losses.

Implications

This work underscores the complex interaction between blockchain infrastructure and AI models, indicating that a careful choice of models and incentive mechanisms is crucial for sustaining decentralized learning without central authority intervention. It suggests that effective self-assessment mechanisms coupled with transparent incentive structures can enable robust and collaborative AI development on blockchain platforms.

Future Directions

Future research could expand on diverse blockchain environments, model types, and real-world data scenarios to enhance understanding of decentralized learning's scalability and effectiveness. Exploring advanced defensive strategies against adversarial actions in decentralized setups could further stabilize these systems.

In conclusion, this comprehensive investigation into AI blockchain integration offers significant insights regarding model deployment strategies, agent dynamics, and cost considerations, paving the way for more democratized and transparent AI ecosystems.