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Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain

Weiliang Chen, Li Jia, Yang Zhou, Qianqian Ren

TL;DR

The paper tackles secure, scalable trajectory prediction in distributed vehicular environments by integrating reputation-driven asynchronous federated learning with a graph-based, DP-enabled data-sharing framework built on a permissioned blockchain. It contributes an interpretable reputation quantization mechanism, a graph neural network–based data-sharing pipeline, and a PPO-driven clustering strategy to prioritize high-quality data providers during aggregation, along with a Proof of Reputation consensus to coordinate updates. The approach leverages a DAG-embedded reputation system and RL-based node grouping to enable efficient, privacy-preserving global model training $M$ from local models $m_i$, including the LP-based privacy mechanism and the loss-based validation ${Loss}^u(P_j)$. Experiments on ApolloScape and NGSIM show improvements in ADE and FDE and robustness to bad nodes, with computation time that remains practical for server-assisted deployment and 6G-ready expansion.

Abstract

Federated learning combined with blockchain empowers secure data sharing in autonomous driving applications. Nevertheless, with the increasing granularity and complexity of vehicle-generated data, the lack of data quality audits raises concerns about multi-party mistrust in trajectory prediction tasks. In response, this paper proposes an asynchronous federated learning data sharing method based on an interpretable reputation quantization mechanism utilizing graph neural network tools. Data providers share data structures under differential privacy constraints to ensure security while reducing redundant data. We implement deep reinforcement learning to categorize vehicles by reputation level, which optimizes the aggregation efficiency of federated learning. Experimental results demonstrate that the proposed data sharing scheme not only reinforces the security of the trajectory prediction task but also enhances prediction accuracy.

Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain

TL;DR

The paper tackles secure, scalable trajectory prediction in distributed vehicular environments by integrating reputation-driven asynchronous federated learning with a graph-based, DP-enabled data-sharing framework built on a permissioned blockchain. It contributes an interpretable reputation quantization mechanism, a graph neural network–based data-sharing pipeline, and a PPO-driven clustering strategy to prioritize high-quality data providers during aggregation, along with a Proof of Reputation consensus to coordinate updates. The approach leverages a DAG-embedded reputation system and RL-based node grouping to enable efficient, privacy-preserving global model training from local models , including the LP-based privacy mechanism and the loss-based validation . Experiments on ApolloScape and NGSIM show improvements in ADE and FDE and robustness to bad nodes, with computation time that remains practical for server-assisted deployment and 6G-ready expansion.

Abstract

Federated learning combined with blockchain empowers secure data sharing in autonomous driving applications. Nevertheless, with the increasing granularity and complexity of vehicle-generated data, the lack of data quality audits raises concerns about multi-party mistrust in trajectory prediction tasks. In response, this paper proposes an asynchronous federated learning data sharing method based on an interpretable reputation quantization mechanism utilizing graph neural network tools. Data providers share data structures under differential privacy constraints to ensure security while reducing redundant data. We implement deep reinforcement learning to categorize vehicles by reputation level, which optimizes the aggregation efficiency of federated learning. Experimental results demonstrate that the proposed data sharing scheme not only reinforces the security of the trajectory prediction task but also enhances prediction accuracy.
Paper Structure (28 sections, 23 equations, 15 figures, 3 tables, 1 algorithm)

This paper contains 28 sections, 23 equations, 15 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. RELATED WORK
  3. SYSTEM MODEL
  4. Formulation of Trajectory Prediction
  5. Our Proposed Architecture And Workflow
  6. Reputation-Based Hybrid Blockchain For Data Sharing
  7. Graph-Based Trajectory Modeling
  8. Differential Privacy-Enhanced Data Sharing
  9. Trajectory Similarity-Based Reputation-Aware Data Sharing
  10. Consensus: Proof of Reputation(PoR)
  11. Trajectory Prediction
  12. Enhanced Asynchronous Federated Learning with PPO Clustering Algorithm Based on Vehicle Reputation
  13. Graph Asynchronous Federated Learning for Trajectory Prediction
  14. Reputation-Based PPO Vehicle Clustering Algorithm
  15. EXPERIMENTS
  16. ...and 13 more sections

Figures (15)

  • Figure 1: Architecture of secure data sharing solution.
  • Figure 2: Working mechanisms for the proposed methodology.
  • Figure 3: Reputation-Aware Data Sharing Mechanism
  • Figure 4: Reputation-driven PPO vehicle clustering algorithm optimizes asynchronous federated learning to complete global model aggregation and improve vehicle local models.
  • Figure 5: The datasets used in the experiments: The vehicle trajectory data in the NGSIM dataset are collected by multiple digital cameras and include different traffic conditions such as light, moderate, and heavy congestion on real highways. Overhead views of the two study areas, US-101 highway U101 and I-80 highway I80, are shown in Fig. (a) and (b), respectively, obtained from Google Maps. The ApolloScape trajectory dataset Apollo consists of data collected by a vehicle named "Apollo acquisition car" during rush hours in urban areas. Our proposed approach is validated in these two different traffic environments.
  • ...and 10 more figures