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REVIEW 3 major objections 5 minor 28 references

Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Grouping vehicles by type and fine-tuning pre-trained models yields faster, more accurate federated learning in digital-twin vehicular networks.

desk verdict Sensible architecture, but the evaluation gives only HFTL a pre-trained model, so the reported gains are transfer learning by construction; the distinct contributions are unvalidated. read the letter →

arxiv 2608.11532 v1 pith:PCNGPGUO submitted 2026-08-12 cs.LG cs.AI

Qasim Zia , Saide Zhu , Haoxin Wang , Zafar Iqbal , Yingshu Li This is my paper · ORCID
classification cs.LGcs.AI
keywords hierarchicalfederatedtransferlearningdigitaltwinvehicularadhocnetworkvehicleclusteringtrustworthinessmetricsblockchainmodelpersonalization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes a training scheme called Hierarchical Federated Transfer Learning (HFTL) for digital-twin vehicular networks. Vehicles are grouped by type—regular cars, emergency vehicles, buses, delivery vehicles, specialized vehicles, motorcycles—and each group fine-tunes a shared pre-trained model on its own private local data. Updates travel hierarchically from edge vehicles through route vehicles to a head vehicle and then to the cloud, where they are weighted by blockchain-recorded data-quality and reputation scores and averaged into the global model. The authors claim this produces higher model accuracy, lower training time, lower resource consumption, lower latency, and faster convergence than centralized learning, federated learning, and clustered federated learning on a real vehicle mobility trace, with only a small increase in communication overhead. If correct, the scheme would give each vehicle type a dedicated, accurate predictive model without sharing raw sensor data.

What carries the argument

The load-bearing mechanism is a two-level aggregation protocol wrapped around pre-trained-model fine-tuning. Algorithm 1 (Weighted Cloud Server Cycling Model Update) has the cloud cycle through the collection of vehicle-type clusters, send the current model to each cluster in turn, and update the global model only when the cluster's average reputation score clears a threshold. Algorithm 2 (Inner Cluster Federated Transfer Learning) organizes each cluster as a chain: a head vehicle receives the pre-trained parameters from the base station, forwards them to route vehicles, which pass them to edge vehicles; each vehicle fine-tunes with its private data and returns the update along with its data quantity and quality score, and the head combines updates weighted by data quantity and score before sending the result back up. The reputation update rule and the threshold check are what keep malicious or low-quality participants out of the global update.

What would settle it

Retrain the three baselines under exactly the same settings as HFTL but give them the same pre-trained initialization, then compare accuracy and convergence time. If federated learning or clustered federated learning with pre-training matches HFTL's 0.822 accuracy and 0.04s convergence, the proposed hierarchy is not the source of the improvement and the central claim is not supported.

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Extended reading notes

Core claim

The claim at the center of the paper is that the combination of vehicle-type clustering, pre-trained-model fine-tuning, and trust-weighted hierarchical aggregation is what makes federated learning work in heterogeneous vehicular settings. Because each cluster starts from a model already trained on a related task and refines it on data from vehicles of one type, the resulting model matches that type's driving patterns and data distribution instead of being diluted by averaging across very different vehicles. The trustworthiness metric—built from data completeness, sensor collaboration, event reporting, format consistency, vehicle health, and safe driving—decides how much weight a vehicle's update gets and can exclude a vehicle entirely once its reputation score falls below a threshold. In the reported experiments, HFTL reaches 0.822 average accuracy versus 0.752 for clustered federated learning, 0.721 for federated learning, and 0.708 for centralized learning, while reducing training time to 1.02s and convergence time to 0.04s; communication overhead is slightly higher than the baselines. The paper interprets these results as evidence that HFTL is well suited to real-time ITS applications like traffic-flow prediction, route optimization, predictive maintenance, and collision alerts.

Load-bearing premise

The evaluation assumes the reported gains come from the hierarchical clustering and trust weighting, but only HFTL starts from a pre-trained model while the baselines train from scratch, so the advantage could be mostly the known benefit of fine-tuning rather than the proposed architecture.

Editorial extensions

If this is right

  • A deployed HFTL system would maintain a distinct predictive model for each vehicle category rather than one global model, so emergency, delivery, and public-transport vehicles each get predictions tuned to their own routes and data.
  • Because each cluster fine-tunes a pre-trained model, training time and computational resource use drop relative to training from scratch, easing the load on vehicles with limited onboard compute.
  • The reputation threshold gives the network a concrete defense against malicious participants: a vehicle whose score falls below the threshold cannot contribute to the global model, and the blockchain prevents post-hoc tampering with those scores.
  • The scalability experiments indicate that as the network grows to hundreds of vehicles, HFTL keeps accuracy near 0.82 while the baseline methods fall to roughly 0.68, so the relative benefit widens at larger scale.
  • The slight communication overhead increase is the price paid for transmitting pre-trained models and personalized updates, and the paper identifies communication compression and asynchronous updates as the levers for reducing it in very large deployments.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An implication the paper leaves implicit: the same cluster-and-fine-tune structure should transfer to other digital-twin settings with behaviorally distinct device classes—industrial fleets, drones, or robots—provided a related pre-trained source model exists.
  • A natural ablation would isolate the contribution of each design choice: compare HFTL against a version with random (non-type) clustering, a version without reputation weighting, and a version trained from scratch, to see which component actually drives the accuracy and convergence gains.
  • Because the data-quality score is computed by the vehicle itself before being stored on the blockchain, a malicious vehicle could in principle inflate its own score; in practice the reputation mechanism would need independent verification of the score components to close that hole.
  • The measured tradeoff suggests a testable boundary: with model compression or asynchronous updates, the communication overhead could drop below standard federated learning while the accuracy and convergence advantages remain, which would make the case for deployment stronger.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes Hierarchical Federated Transfer Learning (HFTL) for Digital Twin-based Vehicular Ad Hoc Networks. Vehicles are clustered by type; a pre-trained model is fine-tuned inside each cluster by head, route, and edge vehicles; and updates are weighted by a data-quality reputation score recorded on a blockchain. The authors present two algorithms (Weighted Cloud Server Cycling Model Update and Inner Cluster Federated Transfer Learning) and evaluate them on a real vehicle mobility trace against Centralized Learning, Federated Learning, and Clustered Federated Learning. Tables 3 and 4 and Figures 4-7 report that HFTL achieves higher accuracy, lower training time, lower resource consumption, lower latency, faster convergence, and higher throughput, at the cost of slightly higher communication overhead.

Significance. If the claimed results were properly supported, HFTL would be a meaningful contribution to personalized, privacy-preserving model training in vehicular networks: per-vehicle-type fine-tuning combined with reputation-based aggregation is a sensible design, and the scalability tests address a practically important regime. The paper also considers multiple performance metrics and uses a real-world mobility trace. However, the current evaluation does not separate the effect of transfer learning from the proposed clustering, digital twin, and trust mechanisms; the reputation system is never experimentally tested; and no uncertainty quantification is provided. The core idea is promising, but the evidence presented is not sufficient to support the central claim.

major comments (3)
  1. [Section 4.1.4 vs. Section 3.1] The central empirical claim rests on a confounded comparison. Section 3.1 states that HFTL fine-tunes a pre-trained model shared according to vehicle type, while Section 4.1.4 describes the baseline algorithms (CL, FL, CFL) as trained with the same hyperparameters but gives no indication that any baseline receives a pre-trained initialization. The discussion in Section 4.1.6 explicitly attributes the advantages to this asymmetry (e.g., 'fine-tuning a model requires less time compared to training a model from scratch' and 'pre-trained models that require less time to make predictions'). Consequently, Tables 3 and 4 and Figures 4 and 6 do not isolate the contributions of clustering, digital twin synchronization, or reputation weighting; the improved accuracy and convergence could be produced by transfer learning alone. Please add a control in which FL and CFL are initialized from the same pre-trained model, and ablate the HFTL-specific components (clustering by type, hierarchical relay, reputation weighting) one at a time.
  2. [Section 3.4 and Section 4] The trustworthiness and reputation mechanism is described but never evaluated. Equations (2)-(7) define the data quality score, reputation update, model-update weight, and exclusion threshold, and Section 3.4 claims that this mechanism keeps malicious vehicles from interfering; however, no experiment in Section 4 varies the presence of malicious vehicles, compares HFTL with and without reputation weighting, or tests the exclusion threshold of Equation (7). The abstract and contribution list therefore claim a security property that the experimental section does not measure.
  3. [Table 3 and Figure 4] The performance comparison lacks uncertainty quantification. The text states that 100 simulations were run to eliminate bias, yet Table 3 and Figure 4 report only average values with no standard deviation, confidence interval, or per-simulation spread. Without such information, the reader cannot assess whether the differences between HFTL (model accuracy 0.822) and CFL (0.752) are stable across the random topologies of Figure 3. Please report the distribution of outcomes.
minor comments (5)
  1. [Equations (2)-(5)] The notation is inconsistent: Equation (2) defines DTS_z, but Equation (3) uses DQS_z; Equation (3) defines RPS_z(t_m), while Equation (5) writes RS_z(t_m). Please unify the symbols.
  2. [Equation (1)] Equation (1) is not a well-formed formal definition; the expression 'DT = ΣΣ Tvj∈Ci' is ambiguous. Please define DT as a set or collection of clusters and state the indexing explicitly.
  3. [Equation (8)] Equation (8) introduces the notation PSr and |v| without formal definitions in the surrounding text; also, the function Fweight(...) used in Algorithm 1 is not defined beyond this equation.
  4. [Section 4.1.2] Section 4.1.2 says there are twelve random clusters each with more than ten vehicles, while Section 4.1.3 says vehicles are 'randomly clustering vehicles according to their similar category'; please clarify whether clusters are homogeneous by type or whether the type assignment is post-hoc.
  5. [Highlights and Figure 1] The manuscript contains unresolved placeholders: 'Research highlight 1' and 'Research highlight 2' after the highlights header, and the label 'Graphical Abstract' before the title. These should be completed or removed.

Circularity Check

2 steps flagged · score 6.0 of 10

The evaluation confounds transfer learning with the proposed HFTL mechanism: only HFTL fine-tunes a pre-trained model, so the reported accuracy, training-time, resource, and latency gains restate the known benefit of fine-tuning rather than testing the hierarchical architecture.

  1. self definitional [Section 4.1.6, Experimental Results and Performance Analysis (discussion of Table 3 and Figure 4)]
    "The Federated transfer learning-based algorithm has the highest model accuracy average because it uses a pre-trained model according to the specific vehicle type. So, its prediction accuracy is better on average than the other two models."

    The paper's own explanation reduces the reported accuracy advantage to the one input that is unique to HFTL: the use of a pre-trained model. Section 3.1 defines HFTL as fine-tuning a pre-trained model, and Section 4.1.4 gives no pre-trained initialization to CL, FL, or CFL. The measurement is therefore not a test of hierarchical clustering, digital twins, or reputation weighting; the outcome is the known benefit of transfer learning restated as an HFTL result.

  2. fitted input called prediction [Section 4.1.6, Experimental Results and Performance Analysis (training time, resource consumption, and latency discussion)]
    "For the performance metric Training Time, on average, of a hundred different simulations, our algorithm takes less time because fine-tuning a model requires less time in seconds compared to training a model from scratch. ... This is due to its use of pre-trained models that require less time to make predictions."

    Training time, resource consumption, and latency are claimed as HFTL advantages, but the paper attributes each one to fine-tuning rather than training from scratch. Since only HFTL receives a pre-trained model, these metric gaps are built into the experimental condition: fine-tuning is faster, cheaper, and lower-latency than from-scratch training by construction. No ablation separates the pre-trained input from the proposed HFTL algorithm, so the numbers do not predict the architecture's own contribution.

full rationale

The paper's central empirical claim is that HFTL outperforms CL, FL, and CFL in accuracy, training time, resource consumption, latency, and convergence. Yet the evaluation is confounded by construction: Section 3.1 defines HFTL around a shared pre-trained model that is fine-tuned on local vehicle data, while Section 4.1.4 describes the baselines only as sharing the same hyperparameters, with no indication of any pre-trained initialization. The paper itself credits the pre-trained model for the observed gains in accuracy, training time, resource consumption, and latency, meaning the reported superiority is primarily a demonstration of the well-known benefit of fine-tuning rather than evidence for the hierarchical clustering, digital-twin synchronization, or reputation-weighting components. Those HFTL-specific components are never ablated, and the blockchain trustworthiness mechanism is described but not evaluated in any experiment. The paper's self-citations (Zia 2015, 2016, 2024) appear only in related-work context and are not load-bearing for the main argument, so this is not a self-citation circularity. The central derivation of superiority, however, reduces by construction to the pre-trained model input, making the empirical claim partially circular. The algorithmic framework itself has independent content in its definitions and pseudocode, which prevents a higher score.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The proposed scheme's central claim rests on several assumptions that are not validated: existence and suitability of pre-trained models, real-time digital twin synchronization, tamper-proof blockchain behavior, and the benefit of sequential intra-cluster model combination. The free parameters of the trust scoring system (w1..w6, alpha, beta, gamma, theta) are never assigned values or tested, and the trust mechanism is not part of the experimental evaluation.

free parameters (5)
  • DTS weights w1..w6 = not specified
    Weights in Equation 2 for the data quality score are never assigned values or tuned in experiments; the trustworthiness mechanism is not evaluated at all.
  • alpha (reputation data quality weight) = not specified
    Equation 3 updates reputation score with alpha and beta, but no values are given or used in the experiments.
  • beta (malicious penalty factor) = not specified
    Equation 3, same as above.
  • gamma (reward scaling factor) = not specified
    Equation 6 converts reputation into rewards; no value is given.
  • theta (exclusion threshold) = not specified
    Equation 7 excludes vehicles with RPS below theta; no value is given.
assumptions (5)
  • ad hoc to paper Each cluster contains exactly one type of vehicle and vehicles are assigned by type.
    Equation 1 and Section 3.1 define clusters by vehicle type; this is a design choice, not derived from data or theory.
  • domain assumption A suitable pre-trained model exists for each vehicle type and target application.
    Section 3.1 states a pre-trained model is shared and fine-tuned but its source and transferability are never specified; the accuracy advantage depends on it.
  • domain assumption The digital twin accurately and synchronously represents the physical vehicle.
    Section 3.2 assumes real-time synchronization; the experiments use a static CSV trace and never exercise the digital twin layer.
  • domain assumption Blockchain provides tamper-proof records and ensures trust scores cannot be maliciously altered.
    Section 3.3 relies on blockchain guarantees; no experiment or proof verifies this property in the proposed architecture.
  • ad hoc to paper The inner-cluster sequential fine-tuning and weighted combination of models across head/route/edge vehicles preserves model quality.
    Algorithm 2 combines fine-tuned models sequentially; this is an unproven modeling assumption that could degrade the model.

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Cite this review

Pith. "Pith review of Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks." pith.science (2026). https://pith.science/paper/PCNGPGUO

@misc{pith2026260811532,
  author       = {Pith},
  title        = {Pith review of: Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PCNGPGUO}},
  note         = {Machine review of arXiv:2608.11532}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2608.11532 by the authors.

Figure 1
Figure 1. High-level architecture of Hierarchical Federated Transfer Learning for digital [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. The Flowchart and Vehicle type data flow for the Inner Cluster of Hierarchical [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Twelve random topologies Network X generated [PITH_FULL_IMAGE:figures/full_fig_p021_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Performance metrics graph results of our proposed Federated Transfer Learn [PITH_FULL_IMAGE:figures/full_fig_p024_4.png]
Figure 5
Figure 5. Figure 5: Graphical Comparison of Different Algorithm’s Model Accuracy, Resource [PITH_FULL_IMAGE:figures/full_fig_p026_5.png]
Figure 6
Figure 6. Figure 6: Scalability of our Federated Transfer Algorithm with Clustered Federated, Fed [PITH_FULL_IMAGE:figures/full_fig_p027_6.png]
Figure 7
Figure 7. Figure 7: Scalability of our Federated Transfer Algorithm with Federated and Centralized [PITH_FULL_IMAGE:figures/full_fig_p028_7.png]

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Reference graph

Works this paper leans on

28 extracted references · 27 canonical work pages

  1. [1]

    Noor-A-Rahim, Z

    M. Noor-A-Rahim, Z. Liu, H. Lee, G. M. N. Ali, D. Pesch, and P. Xiao, ``A survey on resource allocation in vehicular networks,'' IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 2, pp. 701--721, 2020

    work page 2020

  2. [2]

    Q. Zia, M. S. Farooq, and A. Abid, ``Improving response time of vehicular ad hoc networks (VANET),'' 2016

    work page 2016

  3. [3]

    C. He, T. H. Luan, R. Lu, Z. Su, and M. Dong, ``Security and privacy in vehicular digital twin networks: Challenges and solutions,'' IEEE Wireless Communications, 2022

    work page 2022

  4. [4]

    L. U. Khan, E. Mustafa, J. Shuja, F. Rehman, K. Bilal, Z. Han, and C. S. Hong, ``Federated learning for digital twin-based vehicular networks: Architecture and challenges,'' IEEE Wireless Communications, 2023

    work page 2023

  5. [5]

    AbdulRahman, H

    S. AbdulRahman, H. Tout, H. Ould-Slimane, A. Mourad, C. Talhi, and M. Guizani, ``A survey on federated learning: The journey from centralized to distributed on-site learning and beyond,'' IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5476--5497, 2020

    work page 2020

  6. [6]

    B. Li, Y. Wu, J. Song, R. Lu, T. Li, and L. Zhao, ``DeepFed: Federated deep learning for intrusion detection in industrial cyber--physical systems,'' IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5615--5624, 2020

    work page 2020

  7. [7]

    L. U. Khan, W. Saad, Z. Han, and C. S. Hong, ``Dispersed federated learning: Vision, taxonomy, and future directions,'' IEEE Wireless Communications, vol. 28, no. 5, pp. 192--198, 2021

    work page 2021

  8. [8]

    Kone c n \`y , H

    J. Kone c n \`y , H. B. McMahan, F. X. Yu, P. Richt \'a rik, A. T. Suresh, and D. Bacon, ``Federated learning: Strategies for improving communication efficiency,'' arXiv preprint arXiv:1610.05492, 2016

    arXiv 2016

Show all 28 references
  1. [9]

    B. Li, Y. Jiang, Q. Pei, T. Li, L. Liu, and R. Lu, ``FEEL: Federated end-to-end learning with non-IID data for vehicular ad hoc networks,'' IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 16\,728--16\,740, 2022

    2022

  2. [10]

    S. S. Sepasgozar and S. Pierre, ``Fed-NTP: A federated learning algorithm for network traffic prediction in VANET,'' IEEE Access, vol. 10, pp. 119\,607--119\,616, 2022

    2022

  3. [11]

    Zhang and B

    L. Zhang and B. Zhang, ``Hierarchical machine learning---a learning methodology inspired by human intelligence,'' in International Conference on Rough Sets and Knowledge Technology. 1em plus 0.5em minus 0.4em Springer, 2006, pp. 28--30

    2006

  4. [12]

    Gon c alves, J

    F. Gon c alves, J. Macedo, and A. Santos, ``An intelligent hierarchical security framework for VANETs,'' Information, vol. 12, no. 11, p. 455, 2021

    2021

  5. [13]

    Ahmed, U

    M. Ahmed, U. Sardar, S. Ali, S. Alam, M. Patterson, and I. U. Khan, ``Robust brain age estimation via regression models and MRI-derived features,'' International Conference on Computational Collective Intelligence, pp. 661--674, 2023

    2023

  6. [14]

    M. S. HaghighiFard and S. Coleri, ``Hierarchical federated learning in multi-hop cluster-based VANETs,'' arXiv preprint arXiv:2401.10361, 2024

    2024 arXiv

  7. [15]

    Y. Liu, Y. Kang, C. Xing, T. Chen, and Q. Yang, ``A secure federated transfer learning framework,'' IEEE Intelligent Systems, vol. 35, no. 4, pp. 70--82, 2020

    2020

  8. [16]

    Otoum, N

    S. Otoum, N. Guizani, and H. Mouftah, ``On the feasibility of split learning, transfer learning and federated learning for preserving security in ITS systems,'' IEEE Transactions on Intelligent Transportation Systems, 2022

    2022

  9. [17]

    Ahmed, H

    M. Ahmed, H. Chai, H. Wang, H. Venkateswara, and M. Patterson, ``EpiFormer: Learning antigen--antibody interactions for epitope prediction via geometric deep learning,'' arXiv preprint arXiv:2606.04154, 2026

    2026 arXiv

  10. [18]

    Zia, ``A survey of data-centric protocols for wireless sensor networks,'' Computer Science Systems Biology, OMICS Publishing Group, vol

    Q. Zia, ``A survey of data-centric protocols for wireless sensor networks,'' Computer Science Systems Biology, OMICS Publishing Group, vol. 8, no. 3, pp. 127--131, 2015

    2015

  11. [19]

    Wang and Y.-S

    S.-S. Wang and Y.-S. Lin, ``PassCAR: A passive clustering aided routing protocol for vehicular ad hoc networks,'' Computer Communications, vol. 36, no. 2, pp. 170--179, 2013

    2013

  12. [20]

    S. A. Rashid, L. Audah, M. M. Hamdi, and S. Alani, ``Prediction based efficient multi-hop clustering approach with adaptive relay node selection for VANET,'' J. Commun., vol. 15, no. 4, pp. 332--344, 2020

    2020

  13. [21]

    Ahmed, N

    M. Ahmed, N. Taj, I. U. Khan, H. Venkateswara, and M. Patterson, ``ChiMERa-Bench: A benchmark dataset for epitope-specific antibody design,'' ICLR 2026 Workshop on Generative and Experimental Perspectives for Biomolecular Design, 2026

    2026

  14. [22]

    Temurnikar, P

    A. Temurnikar, P. Verma, and G. Dhiman, ``A PSO enable multi-hop clustering algorithm for VANET,'' International Journal of Swarm Intelligence Research (IJSIR), vol. 13, no. 2, pp. 1--14, 2022

    2022

  15. [23]

    Q. Zia, C. Wang, S. Zhu, and Y. Li, ``Priority based inter-twin communication in vehicular digital twin networks,'' International Journal of Parallel, Emergent and Distributed Systems, pp. 1--16, 2024

    2024

  16. [24]

    Dai and Y

    Y. Dai and Y. Zhang, ``Adaptive digital twin for vehicular edge computing and networks,'' Journal of Communications and Information Networks, vol. 7, no. 1, pp. 48--59, 2022

    2022

  17. [25]

    Ahmed, S

    M. Ahmed, S. Ali, A. Jan, I. U. Khan, and M. Patterson, ``Improved graph-based antibody-aware epitope prediction with protein language model-based embeddings,'' International Conference on Computational Advances in Bio and Medical Sciences, pp. 290--302, 2025

    2025

  18. [26]

    L. U. Khan, W. Saad, D. Niyato, Z. Han, and C. S. Hong, ``Digital-twin-enabled 6G: Vision, architectural trends, and future directions,'' IEEE Communications Magazine, vol. 60, no. 1, pp. 74--80, 2022

    2022

  19. [27]

    L. U. Khan, Z. Han, W. Saad, E. Hossain, M. Guizani, and C. S. Hong, ``Digital twin of wireless systems: Overview, taxonomy, challenges, and opportunities,'' IEEE Communications Surveys & Tutorials, vol. 24, no. 4, pp. 2230--2254, 2022

    2022

  20. [28]

    [Online]

    Google Colab, Jun 2022. [Online]. Available: https://colab.research.google.com/

    2022

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Reviewed August 16, 2026 · model on record in the stance chip above.