Learningchain. A Novel Blockchain-Based Meritocratic Marketplace for Training Distributed Machine Learning Models

Abstract

AbstractBeez blockchain is a decentralized AI model network that encourages peer collaboration to improve machine learning accuracy. It’s a peer-to-peer network where peers can submit models with improved precision. The system maintains a decentralized, append-only ledger to record critical information, such as who trained and improved the model, when and by how much, and where to find the most recent model. Beez gives cryptographic tokens to contributors. To demonstrate it, in this paper, we designed a simple Wallet API that isolates low-level node communication across the temporal cluster of compute nodes and enables fine-grained parameter-sharing behavior customization. We created a low-overhead communication layer using a custom blockchain to help developers build computational clusters. This API connects model training and parameter pooling. We want that Beez operations to be stateless and independent of ML. Medicine, reconnaissance, and resource-limited situations benefit from decentralized learning. Decentralization lets nodes learn independently and cooperatively without sharing data. To capitalize on this developing technology, decentralized learning algorithms may differ in clustering connection and individual node sharing. A high-level learning framework can improve codebase uniformity and readability. This open-source library accelerates decentralized learning research. Decentralized learning is still young, so many issues must be addressed before cooperative learning technologies become mainstream.KeywordsBlockchainArtificial intelligenceDistributed systemsDecentralized consensus