B-DMTFL: A Practical and Efficient Framework of Dynamic Multi-Tier Federated Learning Based on Blockchain

The emergence of federated learning as a novel distributed machine learning paradigm has found widespread applications in the field of data security. However, the model aggregation mechanism inherent in federated learning relies on centralised devices, and while the integration of blockchain technology effectively mitigates this issue, it simultaneously introduces several challenges. In practical scenarios, federated client devices have different performance levels and latencies, and the incorporation of blockchain's consensus process significantly hinders the training efficiency of federated learning. Furthermore, clients are often untrustworthy and may exhibit malicious behaviour. To address these challenges, this paper proposes a practical and efficient Blockchain-based Dynamic Multi-Tier Federated Learning (B-DMTFL) framework. We consider the heterogeneity of clients in real-world network scenarios, and design a Dynamic FL computation scheme that facilitates reliable and efficient training within the federated learning process. At the same time, we integrate consistent hashing algorithm to formulate a new consensus protocol, thereby improving both the efficiency of consensus and the robustness of model aggregation. Extensive experiments demonstrate that this framework achieves accelerated convergence in applications that integrate blockchain with federated learning, while leveraging the blockchain's verification mechanism to ensure secure model aggregation and maintain high model accuracy.