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With the advancement of Intelligent Transportation Systems, multimodal data from edge devices like vehicles and Roadside Units has grown exponentially. Traditional centralized processing faces real-time bottlenecks, model convergence issues due to data heterogeneity, and privacy risks, failing to meet the need for high-frequency decision-making. This paper presents DFLChain, a dynamic Federated Learning framework based on DAG-based blockchain. The three-layer architecture enables efficient collaboration: the vehicular layer handles local training and transaction packaging, the blockchain layer uses DAG for asynchronous parallel verification to boost throughput, and the application layer automates tasks via smart contracts. Key innovations include: 1) a hybrid tip selection algorithm that dynamically balances energy consumption and model accuracy, prioritizing low-energy nodes for fast consensus in early stages and high-accuracy nodes for model optimization later; 2) a dual-verification mechanism to resist malicious attacks. Experiments on Non-IID data show DFLChain improves convergence speed by 31.9% and 23.1% over FedAvg and DAG-FL, achieving 89.7% accuracy while balancing resource efficiency and model performance in ITS.

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DFLChain: A DAG-Based Dynamic Federated Learning Framework with Hybrid Tip Prioritization for Trustworthy ITS Data Sharing

  • 会议论文

  • 发表日期：

    2026年

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  • Jianghao Chen,
  • Yushen Li,
  • Shuning Sun,
  • Xiaofeng Chen

摘要

With the advancement of Intelligent Transportation Systems, multimodal data from edge devices like vehicles and Roadside Units has grown exponentially. Traditional centralized processing faces real-time bottlenecks, model convergence issues due to data heterogeneity, and privacy risks, failing to meet the need for high-frequency decision-making. This paper presents DFLChain, a dynamic Federated Learning framework based on DAG-based blockchain. The three-layer architecture enables efficient collaboration: the vehicular layer handles local training and transaction packaging, the blockchain layer uses DAG for asynchronous parallel verification to boost throughput, and the application layer automates tasks via smart contracts. Key innovations include: 1) a hybrid tip selection algorithm that dynamically balances energy consumption and model accuracy, prioritizing low-energy nodes for fast consensus in early stages and high-accuracy nodes for model optimization later; 2) a dual-verification mechanism to resist malicious attacks. Experiments on Non-IID data show DFLChain improves convergence speed by 31.9% and 23.1% over FedAvg and DAG-FL, achieving 89.7% accuracy while balancing resource efficiency and model performance in ITS.