Improved Federated Learning Incentive Mechanism Algorithm Based on Explainable DAG Similarity Evaluation
Year: 2025
Type: article
Abstract: In vehicular networks, inter-vehicle data sharing and collaborative computing improve traffic efficiency and driving experience. However, centralized processing faces challenges with privacy, communication bottlenecks, and real-time performance. This paper proposes a trust assessment mechanism for vehicular federated learning based on graph neural network (GNN) edge weight similarity. An explainable asynchronous federated learning data sharing framework is designed, consisting of permissioned asynchronous federated learning and a locally verifiable directed acyclic graph (DAG). The GNN connection weights perform reputation assessment on edge devices through DAG-based verification, while deep reinforcement learning (DRL) enables explainable node selection to improve asynchronous federated learning efficiency. The proposed explainable incentive mechanism based on GNN edge weight similarity and DAG can not only effectively prevent malicious node attacks but also improve the fairness and explainability of federated learning. Extensive experiments across different participant scales (30–200 nodes), various asynchrony degrees (α = 1–5), and malicious node attack scenarios (up to 50% malicious nodes) demonstrate that our method consistently outperforms state-of-the-art approaches, achieving up to 99.2% accuracy with significant improvements of 1.3–3.1% over existing trust-based federated learning methods and maintaining 95% accuracy even under severe attack conditions. The results show that the proposed scheme performs well in terms of learning accuracy and convergence speed.
Source: Mathematics
DOI: 10.3390/math13213507
Authors: Wenhao Lin, Yang Zhou
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G5241102706
OpenAlex ID: W4415814801