BFLN: A Blockchain-based Federated Learning Model for Non-IID Data

TL;DR

This paper introduces BFLN, a blockchain-enhanced federated learning model designed to improve performance on non-IID data distributions while ensuring data privacy and providing incentives.

Contribution

It proposes a novel blockchain-based federated learning framework with a new aggregation method and incentive algorithm for non-IID data scenarios.

Findings

Improves training accuracy on non-IID data

Provides a sustainable incentive mechanism

Outperforms state-of-the-art models

Abstract

As the application of federated learning becomes increasingly widespread, the issue of imbalanced training data distribution has emerged as a significant challenge. Federated learning utilizes local data stored on different training clients for model training, rather than centralizing data on a server, thereby greatly enhancing the privacy and security of training data. However, the distribution of training data across different clients may be imbalanced, with different categories of data potentially residing on different clients. This presents a challenge to traditional federated learning, which assumes data distribution is independent and identically distributed (IID). This paper proposes a Blockchain-based Federated Learning Model for Non-IID Data (BFLN), which combines federated learning with blockchain technology. By introducing a new aggregation method and incentive algorithm, BFLN enhances the model performance of federated learning on non-IID data. Experiments on public datasets demonstrate that, compared to other state-of-the-art models, BFLN improves training accuracy and provides a sustainable incentive mechanism for personalized federated learning.