DB-FL: DAG blockchain-enabled generalized federated dropout learning

Abstract

To protect user privacy and data security, the integration of Federated Learning (FL) and blockchain has become an emerging research hotspot. However, the limited throughput and high communication complexity of traditional blockchains limit their application in large-scale FL tasks, and the synchronous traditional FL will also reduce the training efficiency. To address these issues, in this paper, we propose a Directed Acyclic Graph (DAG) blockchain-enabled generalized Federated Dropout (FD) learning strategy, which could improve the efficiency of FL while ensuring the model generalization. Specifically, the DAG maintained by multiple edge servers will guarantee the security and traceability of the data, and the Reputation-based Tips Selection Algorithm (RTSA) is proposed to reduce the blockchain consensus delay. Second, the semi-asynchronous training among Intelligent Devices (IDs) is adopted to improve the training efficiency, and a reputation-based FD technology is proposed to prevent overfitting of the model. In addition, a Hybrid Optimal Resource Allocation (HORA) algorithm is introduced to minimize the network delay. Finally, simulation results demonstrate the effectiveness and superiority of the proposed algorithms.