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.

翻译：随着联邦学习的应用日益广泛，训练数据分布不平衡问题已成为一个重要挑战。联邦学习利用存储在不同训练客户端上的本地数据进行模型训练，而非将数据集中存储在服务器上，从而极大地提升了训练数据的隐私性与安全性。然而，不同客户端上的训练数据分布可能不均衡，不同类别的数据可能位于不同的客户端。这对传统联邦学习所假设的数据独立同分布（IID）条件构成了挑战。本文提出了一种面向非独立同分布数据的基于区块链的联邦学习模型（BFLN），该模型将联邦学习与区块链技术相结合。通过引入新的聚合方法与激励算法，BFLN提升了联邦学习在非独立同分布数据上的模型性能。在公开数据集上的实验表明，相较于其他先进模型，BFLN提高了训练精度，并为个性化联邦学习提供了可持续的激励机制。