DAG-EnseFL: DAG-Based Asynchronous Federated Learning With Ensemble Distillation

Jiewei Chen, Da Wu, Shaoyong Guo, Feng Qi, Xuesong Qiu

Abstract: In the industrial Internet of Things (IIoT), blockchain technology has been employed to ensure the trustworthiness of federated learning (FL) services. However, the existing framework that combines blockchain and FL suffers from poor training performance and high computational overhead due to the complex consensus mechanism. Although recent studies have explored architectures that integrate Directed Acyclic Graph (DAG) with FL, the aggregation process in DAG-based multi-branch structures still faces significant challenges due to strong statistical heterogeneity across branches. To accommodate the heterogeneity, this paper proposes a DAG-based asynchronous aggregation framework for decentralized FL services. In this framework, the local models are aggregated with global models in the DAG ledger to form a new transaction block (TB). The verified TB becomes the subsequent node of the tail node in the DAG multi-branch structure. Additionally, an FL model delivery mechanism based on improved ensemble distillation is designed. This mechanism merges the models in verified TBs from multiple branches of the DAG, enhancing the accuracy of the final delivery model without compromising system training efficiency. Extensive ablation and comparative experiments demonstrate that our proposed scheme enhances the training efficiency and accuracy of DAG-FL systems while ensuring the security and trustworthiness.

External IDs:dblp:journals/tbd/ChenWGQQ25