BagChain: A Dual-functional Blockchain Leveraging Bagging-based Distributed Learning

TL;DR

BagChain introduces a blockchain framework that replaces proof-of-work with distributed model training, enabling decentralized ensemble learning with robustness to data heterogeneity and limited resources.

Contribution

This work presents a novel blockchain design integrating bagging-based distributed learning, replacing proof-of-work with model training, and introduces mechanisms to reduce blockchain forking.

Findings

Outperforms traditional blockchain in machine learning tasks

Effective with non-IID and constrained data scenarios

Robust to network delays and heterogeneous data

Abstract

This work proposes a dual-functional blockchain framework named BagChain for bagging-based decentralized learning. BagChain integrates blockchain with distributed machine learning by replacing the computationally costly hash operations in proof-of-work with machine-learning model training. BagChain utilizes individual miners' private data samples and limited computing resources to train potentially weak base models, which may be very weak, and further aggregates them into strong ensemble models. Specifically, we design a three-layer blockchain structure associated with the corresponding generation and validation mechanisms to enable distributed machine learning among uncoordinated miners in a permissionless and open setting. To reduce computational waste due to blockchain forking, we further propose the cross fork sharing mechanism for practical networks with lengthy delays. Extensive experiments illustrate the superiority and efficacy of BagChain when handling various machine learning tasks on both independently and identically distributed (IID) and non-IID datasets. BagChain remains robust and effective even when facing constrained local computing capability, heterogeneous private user data, and sparse network connectivity.