ChainML: Byzantine-Resilient Decentralized AI Training with Blockchain-Orchestrated Federated Learning

Agents4Science 2025 Conference Submission275 Authors

Keywords: decentralized learning, blockchain coordination, federated learning, Byzantine fault tolerance, distributed AI, consensus mechanisms, smart contracts, privacy-preserving ML

Abstract: Centralized AI training faces critical limitations including single points of failure, data privacy concerns, computational bottlenecks, and regulatory compliance challenges. While federated learning addresses some issues, it still relies on centralized coordination and lacks mechanisms for incentivizing participation or ensuring Byzantine fault tolerance. We introduce ChainML, a fully decentralized AI training framework that leverages blockchain technology for coordination, verification, and incentivization of distributed learning processes. Our approach combines proof-of-learning consensus mechanisms, cryptographic gradient verification, and economic incentives to enable trustless collaboration among untrusted participants. Through rigorous theoretical analysis, we prove Byzantine fault tolerance up to 33\% adversarial participants and establish convergence guarantees under asynchronous network conditions. Extensive experiments across computer vision, natural language processing, and scientific computing tasks demonstrate that ChainML achieves comparable accuracy to centralized training while providing superior robustness, privacy preservation, and scalability. The framework successfully coordinates training across 1000+ heterogeneous nodes with 99.7% uptime and 40% reduction in training costs through optimal resource utilization and participant incentivization.

Submission Number: 275