O-DAG Blockchain: Improving Model Parameter Selection for Blockchained Federated Learning

In this correspondence, we propose a novel Ordered Directed Acyclic Graph (O-DAG) blockchain to improve model parameter selection for Blockchained Federated Learning (BFL) in Intelligent Transportation Systems (ITS). Our ODAG blockchain composes an innovative Ordered Transaction Topology (OTT) and a Deep reinforcement learning-enhanced Consensus Mechanism (DCM). The OTT ensures the traceability of BFL model parameter selection by using blockchain to sequentially record local and global model parameters. By establishing traceable iteration relationships among these parameters, the OTT facilitates the efficient selection of updated model parameters for BFL. Building upon the OTT, our DCM accelerates BFL convergence by enabling adaptive and fast model parameter selection. The DCM optimizes parameter selection policies for both local training and global aggregation by getting evaluations from heterogeneous ITS clients. Simulations show that our ODAG blockchain outperforms state-of-the-art baselines, improving BFL convergence speed by 38.64% and model accuracy by 7.47%, while maintaining strong scalability in ITS networks