Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks

This paper proposes a novel Hierarchical Federated Transfer Learning (HFTL) architecture for Digital Twin-based Vehicular Ad hoc Networks (DT-VANET) to address challenges of data heterogeneity and data sparsity among vehicles, which cause suboptimal accuracy in federated learning models. The authors state: "Federated learning struggles to adequately train a global model when confronted with data heterogeneity and data sparsity among vehicles, which ensure suboptimal accuracy in making precise predictions for different vehicle types." To solve this, they combine Federated Transfer Learning (FTL) with vehicle clustering based on vehicle types, constructing a framework with two main layers: a physical layer and a digital twin layer. The architecture includes two algorithms: a Weighted Cloud Server Cycling Model Update algorithm for cloud server updates and an Inner Cluster Hierarchical Federated Transfer Learning algorithm for intra-cluster training. The paper also introduces a data quality score-based trustworthiness mechanism, where Every vehicle will calculate its data quality score depending on data completeness, sensor collaboration, events, constant format, the vehicle's general health, and safe, reliable driving patterns, and these scores are stored on blockchain to prevent malicious vehicles from affecting the global model. The clustering groups vehicles into categories including regular vehicles, emergency vehicles, public transportation vehicles, delivery vehicles, specialized vehicles, and two-wheel regular vehicles, with the most advanced vehicle selected as cluster head. Within each cluster, vehicles are further divided into head vehicles, route vehicles, and edge vehicles for hierarchical training. Experiments were conducted on a real-world vehicle mobility trace dataset with twelve random cluster topologies, comparing HFTL against Centralized Learning (CL), Federated Learning (FL), and Clustered Federated Learning (CFL). Results show HFTL achieves the highest model accuracy (0.822 average vs 0.708 for CL, 0.721 for FL, 0.752 for CFL), lowest training time (1.02s vs 2.03s, 1.53s, 1.46s), lowest resource consumption (8.30% vs 20.19%, 15.65%, 14.96%), lowest latency (2.240s vs 2.323s, 2.314s, 2.304s), fastest convergence time (0.04s vs 0.23s, 0.12s, 0.08s), and highest throughput (6.35 vs 6.20, 6.23, 6.24). However, HFTL has slightly higher communication overhead (6.28 vs 6.19, 6.25, 6.21) due to pre-trained model fine-tuning requirements. Scalability testing with up to 500 vehicles shows HFTL maintains superior performance in accuracy (0.823 vs 0.682, 0.689, 0.68), training time (2.78s vs 6.01s, 6.07s, 5.96s), resource consumption (25.87% vs 54.06%, 54.73%, 53.61%), convergence time (29.29s vs 45.82s, 45.72s, 45.46s), and throughput (6.91 vs 6.79, 6.81, 6.84), though communication overhead remains slightly higher. The paper acknowledges limitations including data heterogeneity issues, network limitations in unstable environments, potential model bias from biased local data, slow adaptation to dynamic contexts, and struggles in sparsely populated regions. Future work will explore communication compression techniques, asynchronous model update strategies, and lightweight blockchain implementations for large-scale vehicular networks.

Improvements to AI Systems:

1. Domain-Aware Federated Clustering: Implement a clustering mechanism that groups participating clients by functional roles (e.g., vehicle type, device category) before federated training. This reduces data heterogeneity by ensuring each cluster trains on semantically similar data distributions, improving global model accuracy for specialized subgroups.

2. Hierarchical Two-Tier Aggregation: Use a two-level aggregation scheme—intra-cluster local aggregation followed by inter-cluster cloud aggregation. This reduces communication frequency and training time while preserving personalized model updates for each cluster, enabling faster convergence in large-scale, distributed environments.

3. Trustworthy Client Selection via Data Quality Scoring: Integrate a blockchain-verifiable data quality score (based on completeness, consistency, sensor reliability, and operational health) to filter out malicious or low-quality clients before model updates. This prevents poisoning attacks and biased gradients, improving model robustness and fairness.

4. Pre-trained Model Fine-Tuning for Sparse Data: Apply transfer learning by initializing each cluster’s model with a pre-trained global model, then fine-tuning on cluster-specific data. This addresses data sparsity, allowing the system to achieve high accuracy even when individual clients have limited or non-IID data.

5. Dynamic Cluster Head Selection: Automatically designate the most capable client (e.g., highest computational power, best connectivity) as the cluster head for aggregation. This optimizes resource usage and reduces latency, as the head handles local model merging before sending updates to the cloud.

6. Scalable Asynchronous Updates for Dynamic Environments: Replace synchronous global synchronization with asynchronous, event-driven model updates within clusters. This allows the system to adapt to changing client availability and network conditions (e.g., vehicles moving in/out of range), improving stability in unstable or sparse environments.

What the Improved AI System Can Do:

• Achieve higher prediction accuracy (e.g., 0.822 vs 0.708 for centralized) in heterogeneous, non-IID data settings by grouping similar clients and personalizing models per cluster.

• Reduce training time by up to 50% (e.g., 1.02s vs 2.03s) and resource consumption by over 60% (8.3% vs 20.2%) through hierarchical aggregation and efficient cluster heads.

• Maintain robust performance at scale (up to 500+ clients) with lower convergence time (29.29s vs 45.82s) and higher throughput (6.91 vs 6.79), making it suitable for large IoT, vehicular, or edge networks.

• Resist malicious attacks and data poisoning via blockchain-verified quality scores, ensuring trustworthy model updates.

• Operate effectively in sparse or unstable environments by leveraging pre-trained transfer learning and asynchronous updates, avoiding performance collapse when data is scarce or clients are intermittent.

• Provide faster, more reliable real-time predictions (e.g., for traffic management, autonomous driving, or smart city applications) with lower latency (2.240s vs 2.323s) and minimal communication overhead trade-offs.

In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy. However, Federated learning struggles to adequately train a global model when confronted with data heterogeneity and data sparsity among vehicles, which ensure suboptimal accuracy in making precise predictions for different vehicle types. To address these challenges, this paper combines Federated Transfer Learning (FTL) to conduct vehicle clustering related to types of vehicles and proposes a novel Hierarchical Federated Transfer Learning (HFTL). We construct a framework for DT-VANET, along with two algorithms designed for cloud server model updates and intra-cluster federated transfer learning, to improve the accuracy of the global model. In addition, we developed a data quality score-based mechanism to prevent the global model from being affected by malicious vehicles. Lastly, detailed experiments on real-world datasets are conducted, considering different performance metrics that verify the effectiveness and efficiency of our algorithm.