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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

T0 review · 5 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This survey argues that edge general intelligence will come from multiple collaborating large language models rather than a single model, and it maps the architectures, orchestration techniques, and trust mechanisms needed to make such…

desk verdict Useful survey of multi-LLM for edge, but the self-cited trust case study runs on a server and the edge-feasibility premise is assumed, so treat it as a taxonomy reference rather than evidence. read the letter →

arxiv 2507.00672 v1 pith:7ZT5IHQ4 submitted 2025-07-01 cs.NI cs.DC

Haoxiang Luo , Yinqiu Liu , Ruichen Zhang , Jiacheng Wang , Gang Sun , Dusit Niyato , Hongfang Yu , Zehui Xiong
show 2 more authors
Xianbin Wang Xuemin Shen
This is my paper
classification cs.NIcs.DC
keywords multi-LLMsystemsedgegeneralintelligencecomputinglargelanguagemodelstrustworthyAIorchestrationmultimodalfusionblockchainconsensus
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

The paper sets out to establish that multi-LLM systems are the credible path to Edge General Intelligence (EGI), the vision in which edge nodes such as sensors, vehicles, drones, and city infrastructure reason as flexibly as a generalist AI. It claims that single LLMs, despite their versatility, hallucinate, carry biases, and cannot adapt across heterogeneous edge contexts, while teams of specialized LLMs can cross-verify outputs, decompose tasks, fuse modalities, and keep sensitive data local. The survey therefore organizes the field into three collaboration modes and six enabling technology categories, and it argues that trustworthiness is the central obstacle, proposing blockchain-driven consensus as a concrete mechanism. The practical stake is that reliable, low-latency intelligence could move from cloud data centers to the edge itself.

What carries the argument

The load-bearing organizing device is a taxonomy that classifies multi-LLM collaboration into cooperative, competitive (adversarial), and ensemble modes, and then maps each application scenario onto enabling technologies. The six enabling technology categories, model compression, dynamic resource orchestration, model context protocol, privacy protection, LLM fine-tuning, and multimodal information fusion, carry the argument that edge constraints can be satisfied. The trust argument runs through a blockchain-driven multi-LLM design in which response generation, peer broadcast, consensus-based answer selection, block formation, and ledger extension make the system tamper-evident. The named mechanism in the case study is weighted Byzantine fault tolerance (WBFT), a consensus where each LLM's voting weight depends on its content-generation ability and credibility.

What would settle it

Equip a realistic edge node with a compressed LLM and a multi-LLM team, for instance several small specialist models plus a router, then run the same safety-critical workload and record accuracy, latency, energy, and memory. If the multi-LLM setup is never competitive with the single compressed model on those metrics, or if the coordination overhead cancels the gains, the survey's central motivation is refuted.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim is that no existing survey had examined the unique opportunities and challenges of multiple LLMs working together inside edge networks, and that this gap is now filled by a systematic review. The paper identifies the progression from narrow edge AI models, to single-LLM deployment, to multi-LLM systems, and argues the last is the only one that combines generalization, specialization, robustness, and privacy for dynamic edge tasks. It further claims that the key enabling technologies form six strands: model compression, dynamic resource orchestration, model context protocol, privacy protection, fine-tuning, and multimodal information fusion, and that trust, not just performance, is what determines whether multi-LLM edge systems can serve safety-critical applications. The tutorial case study asserts that blockchain consensus, particularly a weighted Byzantine fault tolerance scheme, can select the most credible answer among competing LLMs and make the collaboration auditable.

Load-bearing premise

The survey assumes, without an end-to-end measurement, that several LLMs can actually run on edge devices within their latency, memory, and energy budgets; if that premise fails, the entire multi-LLM-at-edge program lacks a practical foundation.

Editorial extensions

If this is right

  • If the survey's picture is right, edge nodes could run teams of small, specialized LLMs that together match or exceed a single larger model on reasoning and coverage, while keeping inference local and private.
  • Model context protocols and shared memory banks would let edge LLMs reuse each other's context, reducing redundant computation and enabling cross-device knowledge flow without cloud round-trips.
  • Cascaded inference, where a small model handles easy queries and only hard cases escalate to a larger model, becomes a default design pattern for latency- and energy-constrained multi-LLM deployments.
  • Blockchain-style consensus with quality-weighted voting would make multi-LLM answers verifiable and traceable, which matters for elderly care alerts, grid alarms, traffic decisions, and drone deconfliction.
  • Federated and split fine-tuning would let many edge devices adapt shared LLMs to local tasks without exposing raw user data.

Reading between the lines

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

  • A testable next step the paper leaves implicit: benchmark a genuinely end-to-end multi-LLM edge system against a single compressed LLM on identical hardware, measuring accuracy, latency, energy, and memory. If the team does not win on at least some realistic workloads, the motivation weakens.
  • The trust case currently rests on user ratings of a blockchain-weighted ensemble; an extension would measure manipulation resistance under active attackers who control one or more LLM nodes, since Byzantine fault tolerance claims are testable in simulation.
  • The survey's performance-resource-security triangle suggests a design space in which orchestration policies could be optimized explicitly for that trade-off, rather than treating trust as a separate layer bolted on after performance.
  • The same architecture could transfer to agentic AI at the edge, where LLM agents negotiate tasks; the multi-LLM collaboration taxonomy might serve as a vocabulary for describing agent protocols.
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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

5 major / 5 minor

Summary. This survey positions multiple LLMs collaborating at the network edge as the path to Edge General Intelligence (EGI). It contrasts traditional AI, single LLM, and multi-LLM systems, sketches four applications (elderly care, smart grid inspection, intelligent transportation, low-altitude economy networks), organizes enabling technologies into six categories, and presents a blockchain-based trust case study with user ratings and a table of open-source datasets. The stated contribution is to fill the gap of a comprehensive treatment of multi-LLM systems in edge networks.

Significance. If the survey's underlying feasibility assumption is granted, it offers a useful and unusually broad map of an emerging topic, and its taxonomy of collaboration modes and enabling technologies is a reasonable organizing device. The structured comparison with related surveys and the consolidated dataset list are helpful starting points. The paper does not ship machine-checked proofs or reproducible code, and its only quantitative evidence is a small self-cited user study run on a server; the significance of the survey therefore rests on the plausibility of the surveyed techniques transferring to edge hardware, which the manuscript does not establish.

major comments (5)
  1. [Section V-A and Fig. 10] The only quantitative support for the paper's central premise is the WBFT case study, which is drawn from the authors' prior works [27] and [39] and runs on a 96-core Xeon server with 1 TB memory, not on edge-class hardware. The user evaluation uses 15 volunteers and reports average ratings without error bars or significance tests. Since Contribution 3 explicitly claims a case study for deploying multi-LLM systems at the edge, this mismatch is load-bearing; the authors should either add edge-relevant resource measurements (memory, latency, energy) for several LLMs running concurrently on representative edge devices or explicitly reframe the case study as an illustrative server-based demonstration and state that edge deployment remains open.
  2. [Section IV.A-IV.G] The enabling-technology sections catalog mostly single-LLM compression and cloud-edge collaboration methods (EdgeBERT, CE-CoLLM, Hybrid LLM, EdgeShard, FedIT, SplitLoRA, and others) and assert their relevance to multi-LLM edge systems without providing an end-to-end measurement of multiple LLMs running simultaneously on edge devices. The survey therefore assumes, rather than demonstrates, the practical feasibility that motivates the entire topic. The authors should either include existing multi-LLM edge deployments with resource data or clearly mark these techniques as transferable single-model tools and add an explicit statement that end-to-end multi-LLM edge feasibility is an open question.
  3. [Section I-C and Table I] The claimed novelty that 'there has been no comprehensive investigation into the unique opportunities and challenges of multiple LLMs working together in edge networks' is not fully supported by Table I. Several related surveys already address LLM deployment at the edge (e.g., [2], [31], [33], [35]) and multi-LLM collaboration (e.g., [24], [36], [37]); what is missing is a sharper statement of how this survey's synthesis differs beyond combining two literatures. The authors should clarify the specific gap and qualify 'comprehensive' in light of the speculative, non-deployed application scenarios in Section III.
  4. [Section V-A] The trust case study is under-specified: the text does not explain how response quality and trust values are computed, how the WBFT voting weights alpha and beta are chosen, what exactly the baselines 'single LLM' and 'multi-LLM without blockchain' are, or whether the 15-volunteer ratings in Fig. 10 are statistically robust. Because this material is self-cited from [27] and [39] with no independent replication, the statement that the comparison 'fully demonstrates' the advantage of trustworthy multi-LLM is too strong. The authors should add methodological detail and a limitations paragraph, or reduce the strength of the claim.
  5. [Section V-B and Table III] The open-source dataset summary does not support the contribution claim of datasets 'for deploying multi-LLM systems at the edge.' MMLU, GSM8K, and ChatGLM are standard general-purpose LLM benchmarks or resources, not multi-LLM edge benchmarks, and the table provides no information on edge relevance, resource usage, or multi-model interaction. The authors should either replace or augment the table with datasets and benchmarks actually used in multi-LLM edge research, or descope the contribution accordingly.
minor comments (5)
  1. [Section IV.E] The text refers to the FedIT framework 'as show in Fig. 10,' but the FedIT figure is Fig. 8, while Fig. 10 is the user-satisfaction comparison in Section V-A; please correct the cross-reference.
  2. [Throughout] The term 'LLMS' is inconsistently capitalized (e.g., Sections II-B-2 and V-A), and 'UA Vs' appears instead of 'UAVs' in Section III-B and Section III-D; a terminology pass is needed.
  3. [Section II-B-2] The sentence 'Single LLM refers to a general-purpose intelligence may based on the Transformer architecture' is ungrammatical and should be rewritten.
  4. [Fig. 3] The embedded text in Fig. 3 includes awkward phrases such as 'It exists a generation bias' and 'illusory results'; since this figure is central to the comparison, the text should be cleaned up.
  5. [Section VII] The conclusion repeats 'dynamic data' at the end of the second sentence; this appears to be a truncation of 'dynamic orchestration' and should be fixed.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the survey is a literature review whose trust case study is a disclosed self-cited tutorial, not a derivation that reduces to its own inputs.

full rationale

The paper is a survey rather than a derivation-driven research paper: it organizes existing work into categories, summarizes applications, and provides a case study. Its central claim, that multi-LLM systems offer a path to Edge General Intelligence, is supported by a broad set of external references throughout Sections II, III, and IV. The only sustained use of the authors' own prior work is the trust case study in Section V.A, which is explicitly presented as a tutorial: 'Here, based on the work in [39], we introduce how blockchain drives multi-LLM collaboration,' and 'Luo et al. [27] designed a Weighted Byzantine Fault Tolerance consensus.' These are disclosed citations, not hidden reductions: the survey does not claim to derive the blockchain consensus or the satisfaction ratings from its own framework. The WBFT parameters alpha and beta are experimental variables compared in Fig. 10, not fitted parameters renamed as predictions, and the paper does not assert a uniqueness theorem or rely on a self-citation chain to forbid alternatives. The absence of an end-to-end edge deployment measurement is a limitation of the survey's practical evidence base, but it is a matter of missing validation, not circularity. Accordingly, the derivation chain is self-contained as a literature review, and no specific circular step can be exhibited.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The survey's central claims rest on domain assumptions about LLM collaboration benefits and edge feasibility, plus a hand-chosen parameter in the self-cited blockchain case study. No new entities are introduced.

free parameters (1)
  • WBFT voting weight split (alpha, beta) = alpha = 0.4/0.5/0.6; beta = 0.6/0.5/0.4
    Chosen by hand in the self-cited case study [27] to balance generation capability against credibility; Fig. 10 satisfaction rankings change with these values and no principled selection procedure is given.
assumptions (3)
  • domain assumption Multi-LLM collaboration improves reliability, reduces hallucination and bias.
    Core premise of Sections II and III, supported only by citations to prior work; no independent experiment in this survey.
  • domain assumption Blockchain consensus selects the best LLM response and makes the system trustworthy.
    Adopted in Section V-A from [27] and [39]; not independently validated, and overhead costs are not quantified.
  • domain assumption Edge devices can host multiple LLMs within resource constraints.
    Sections IV.A to IV.G cite compression, split inference, and orchestration, but no end-to-end edge deployment is measured.

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Cite this review

Pith. "Pith review of Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration." pith.science (2026). https://pith.science/paper/7ZT5IHQ4

@misc{pith2026250700672,
  author       = {Pith},
  title        = {Pith review of: Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7ZT5IHQ4}},
  note         = {Machine review of arXiv:2507.00672}
}
read the original abstract

Edge computing enables real-time data processing closer to its source, thus improving the latency and performance of edge-enabled AI applications. However, traditional AI models often fall short when dealing with complex, dynamic tasks that require advanced reasoning and multimodal data processing. This survey explores the integration of multi-LLMs (Large Language Models) to address this in edge computing, where multiple specialized LLMs collaborate to enhance task performance and adaptability in resource-constrained environments. We review the transition from conventional edge AI models to single LLM deployment and, ultimately, to multi-LLM systems. The survey discusses enabling technologies such as dynamic orchestration, resource scheduling, and cross-domain knowledge transfer that are key for multi-LLM implementation. A central focus is on trusted multi-LLM systems, ensuring robust decision-making in environments where reliability and privacy are crucial. We also present multimodal multi-LLM architectures, where multiple LLMs specialize in handling different data modalities, such as text, images, and audio, by integrating their outputs for comprehensive analysis. Finally, we highlight future directions, including improving resource efficiency, trustworthy governance multi-LLM systems, while addressing privacy, trust, and robustness concerns. This survey provides a valuable reference for researchers and practitioners aiming to leverage multi-LLM systems in edge computing applications.

Figures

Figures reproduced from arXiv: 2507.00672 by the authors.

Figure 1
Figure 1. Key features for EGI, multi-LLM, and multi-LLM for EGI. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Structure of our survey. D. Structure of This Paper The structure of this survey is outlined in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparison with traditional AI, single LLM, and multi-LLM system. Traditional AI models require targeted training based on the particularity of [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Application scenarios of multi-LLM. It includes typical edge scenarios such as elderly care, smart grid inspection, intelligent transportation, and [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: LLM integrating split training in 6G Mobile Edge Computing (MEC) in [ [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: The framework of EdgeLLM in [79]. It includes offline analysis, task scheduling optimization, and online collaborative LLM inference. interactions and a shared memory for knowledge that can benefit others. When an LLM completes a query, it decides whether to write the …
Figure 7
Figure 7. Figure 7: Distributed MoA system model in [130]. Each device has its local prompt and makes inferences through the local LLM. Meanwhile, these prompts are sent to neighboring LLMs for reasoning, and then their responses are aggregated by one LLM. sent to external servers, thereb…
Figure 8
Figure 8. Figure 8: FedIT framework in [142]. All the dense layers of each LLM are introduced into a parallel LoRA module. Thus, the number of trainable parameters is significantly reduced, thereby lowering the computational and communication overhead. LLM can process specific modalities …
Figure 9
Figure 9. Figure 9: The blockchain-driven trustworthy multi-LLM system in [ [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: The comparison of users’ ratings of various LLM schemes in [ [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]

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Reference graph

Works this paper leans on

177 extracted references · 17 canonical work pages · cited by 6 Pith papers

  1. [27]

    A weighted byzantine fault tolerance consensus driven trusted multiple large language models network,

    H. Luo, G. Sun, Y . Liu, D. Zhao, D. Niyato, H. Yu, and S. Dustdar, “A weighted byzantine fault tolerance consensus driven trusted multiple large language models network,”arXiv preprint arXiv:2505.05103, 2025

    arXiv 2025

  2. [39]

    A trustworthy multi-llm network: Challenges, solutions, and a use case,

    H. Luo, G. Sun, Y . Liu, D. Niyato, H. Yu, M. Atiquzzaman, and S. Dustdar, “A trustworthy multi-llm network: Challenges, solutions, and a use case,”arXiv preprint arXiv:2505.03196, 2025

    arXiv 2025

  3. [2]

    Towards edge general intelligence via large language models: Opportunities and challenges,

    H. Chen, W. Deng, S. Yang, J. Xu, Z. Jiang, E. C. Ngai, J. Liu, and X. Liu, “Towards edge general intelligence via large language models: Opportunities and challenges,”IEEE Network, 2025

    2025

  4. [31]

    A review on edge large language models: Design, execution, and applications,

    Y . Zheng, Y . Chen, B. Qian, X. Shi, Y . Shu, and J. Chen, “A review on edge large language models: Design, execution, and applications,” ACM Computing Surveys, vol. 57, no. 8, pp. 1–35, 2025

    2025

  5. [33]

    Mobile edge intelligence for large language models: A contemporary survey,

    G. Qu, Q. Chen, W. Wei, Z. Lin, X. Chen, and K. Huang, “Mobile edge intelligence for large language models: A contemporary survey,” IEEE Communications Surveys & Tutorials, 2025. 20

    2025

  6. [35]

    Pushing large language models to the 6g edge: Vision, challenges, and oppor- tunities,

    Z. Lin, G. Qu, Q. Chen, X. Chen, Z. Chen, and K. Huang, “Pushing large language models to the 6g edge: Vision, challenges, and oppor- tunities,”arXiv preprint arXiv:2309.16739, 2023

    arXiv 2023

  7. [24]

    Merge, ensemble, and cooperate! a survey on collaborative strategies in the era of large language models,

    J. Lu, Z. Pang, M. Xiao, Y . Zhu, R. Xia, and J. Zhang, “Merge, ensemble, and cooperate! a survey on collaborative strategies in the era of large language models,”arXiv preprint arXiv:2407.06089, 2024

    arXiv 2024

  8. [36]

    Harnessing multiple large language models: A survey on llm ensemble,

    Z. Chen, J. Li, P. Chen, Z. Li, K. Sun, Y . Luo, Q. Mao, D. Yang, H. Sun, and P. S. Yu, “Harnessing multiple large language models: A survey on llm ensemble,”arXiv preprint arXiv:2502.18036, 2025

    arXiv 2025

  9. [37]

    When one llm drools, multi-llm collaboration rules,

    S. Feng, W. Ding, A. Liu, Z. Wang, W. Shi, Y . Wang, Z. Shen, X. Han, H. Lang, C.-Y . Leeet al., “When one llm drools, multi-llm collaboration rules,”arXiv preprint arXiv:2502.04506, 2025

    arXiv 2025

Show all 177 references
  1. [1]

    Espd-lp: Edge service pre- deployment based on location prediction in mec,

    L. Song, G. Sun, H. Yu, and D. Niyato, “Espd-lp: Edge service pre- deployment based on location prediction in mec,”IEEE Transactions on Mobile Computing, 2025

    2025

  2. [3]

    Edge intelligence: The confluence of edge computing and artificial intelligence,

    S. Deng, H. Zhao, W. Fang, J. Yin, S. Dustdar, and A. Y . Zomaya, “Edge intelligence: The confluence of edge computing and artificial intelligence,”IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7457– 7469, 2020

    2020

  3. [4]

    Generative ai in cybersecurity: A comprehensive review of llm applications and vulnerabilities,

    M. A. Ferrag, F. Alwahedi, A. Battah, B. Cherif, A. Mechri, N. Ti- hanyi, T. Bisztray, and M. Debbah, “Generative ai in cybersecurity: A comprehensive review of llm applications and vulnerabilities,”Internet of Things and Cyber-Physical Systems, 2025

    2025

  4. [5]

    Generative ai-driven semantic communication networks: Architecture, technologies and applications,

    C. Liang, H. Du, Y . Sun, D. Niyato, J. Kang, D. Zhao, and M. A. Imran, “Generative ai-driven semantic communication networks: Architecture, technologies and applications,”IEEE Transactions on Cognitive Com- munications and Networking, vol. 11, no. 1, pp. 27–47, 2025

    2025

  5. [6]

    A survey on applications of large language model-driven digital twins for intelligent network optimization,

    Z. Guo, F. Tang, L. Luo, M. Zhao, and N. Kato, “A survey on applications of large language model-driven digital twins for intelligent network optimization,”IEEE Communications Surveys & Tutorials, 2025

    2025

  6. [7]

    A survey on evaluation of large language models,

    Y . Chang, X. Wang, J. Wang, Y . Wu, L. Yang, K. Zhu, H. Chen, X. Yi, C. Wang, Y . Wanget al., “A survey on evaluation of large language models,”ACM Transactions on Intelligent Systems and Technology, vol. 15, no. 3, pp. 1–45, 2024

    2024

  7. [8]

    Generative ai for secure physical layer communications: A survey,

    C. Zhao, H. Du, D. Niyato, J. Kang, Z. Xiong, D. I. Kim, X. Shen, and K. B. Letaief, “Generative ai for secure physical layer communications: A survey,”IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 1, pp. 3–26, 2025

    2025

  8. [9]

    Generative ai-enabled vehicular networks: Fundamentals, framework, and case study,

    R. Zhang, K. Xiong, H. Du, D. Niyato, J. Kang, X. Shen, and H. V . Poor, “Generative ai-enabled vehicular networks: Fundamentals, framework, and case study,”IEEE Network, vol. 38, no. 4, pp. 259–267, 2024

    2024

  9. [10]

    The road toward general edge intelligence: Standing on the shoulders of foundation models,

    L. He, L. Fan, X. Lei, P. Fan, A. Nallanathan, and G. K. Karagiannidis, “The road toward general edge intelligence: Standing on the shoulders of foundation models,”IEEE Communications Magazine, 2025

    2025

  10. [11]

    Embodied ai-enhanced vehicular networks: An integrated large language models and reinforcement learning method,

    R. Zhang, C. Zhao, H. Du, D. Niyato, J. Wang, S. Sawadsitang, X. Shen, and D. I. Kim, “Embodied ai-enhanced vehicular networks: An integrated large language models and reinforcement learning method,”IEEE Transactions on Mobile Computing, 2025

    2025

  11. [12]

    Edge large ai models: Collaborative deployment and iot applications,

    Z. Wang, Y . Shi, K. Letaiefet al., “Edge large ai models: Collaborative deployment and iot applications,”arXiv preprint arXiv:2505.03139, 2025

    2025 arXiv

  12. [13]

    Toward democratized generative ai in next-generation mobile edge networks,

    R. Zhang, J. He, X. Luo, D. Niyato, J. Kang, Z. Xiong, Y . Li, and B. Sikdar, “Toward democratized generative ai in next-generation mobile edge networks,”IEEE Network, 2025

    2025

  13. [14]

    Llm-pruner: On the structural pruning of large language models,

    X. Ma, G. Fang, and X. Wang, “Llm-pruner: On the structural pruning of large language models,”Advances in neural information processing systems, vol. 36, pp. 21 702–21 720, 2023

    2023

  14. [15]

    Efficient llms for edge devices: Pruning, quantization, and distillation techniques,

    R. Agrawal, H. Kumar, and S. R. Lnu, “Efficient llms for edge devices: Pruning, quantization, and distillation techniques,” in2025 Interna- tional Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025, pp. 1413–1418

    2025

  15. [16]

    Prima. cpp: Speeding up 70b-scale llm inference on low-resource everyday home clusters,

    Z. Li, T. Li, W. Feng, M. Guizani, and H. Yu, “Prima. cpp: Speeding up 70b-scale llm inference on low-resource everyday home clusters,” arXiv preprint arXiv:2504.08791, 2025

    2025 arXiv

  16. [17]

    Tpi-llm: Serving 70b- scale llms efficiently on low-resource edge devices,

    Z. Li, W. Feng, M. Guizani, and H. Yu, “Tpi-llm: Serving 70b- scale llms efficiently on low-resource edge devices,”arXiv preprint arXiv:2410.00531, 2024

    2024 arXiv

  17. [18]

    An edge-cloud collaboration framework for generative ai service provision with synergetic big cloud model and small edge models,

    Y . Tian, Z. Zhang, Y . Yang, Z. Chen, Z. Yang, R. Jin, T. Q. Quek, and K.-K. Wong, “An edge-cloud collaboration framework for generative ai service provision with synergetic big cloud model and small edge models,”IEEE Network, vol. 38, no. 5, pp. 37–46, 2024

    2024

  18. [19]

    Knowledge fusion of large language models,

    F. Wan, X. Huang, D. Cai, X. Quan, W. Bi, and S. Shi, “Knowledge fusion of large language models,”arXiv preprint arXiv:2401.10491, 2024

    2024 arXiv

  19. [20]

    A multi-llm debiasing framework,

    D. M. Owens, R. A. Rossi, S. Kim, T. Yu, F. Dernoncourt, X. Chen, R. Zhang, J. Gu, H. Deilamsalehy, and N. Lipka, “A multi-llm debiasing framework,”arXiv preprint arXiv:2409.13884, 2024

    2024 arXiv

  20. [21]

    Easy2hard- bench: Standardized difficulty labels for profiling llm performance and generalization,

    M. Ding, C. Deng, J. Choo, Z. Wu, A. Agrawal, A. Schwarzschild, T. Zhou, T. Goldstein, J. Langford, A. Anandkumaret al., “Easy2hard- bench: Standardized difficulty labels for profiling llm performance and generalization,”Advances in Neural Information Processing Systems, vol. ...

    2024

  21. [22]

    Wireless hallucination in generative ai-enabled communications: Concepts, issues, and solutions,

    X. Wang, J. Wang, L. Feng, D. Niyato, R. Zhang, J. Kang, Z. Xiong, H. Du, and S. Mao, “Wireless hallucination in generative ai-enabled communications: Concepts, issues, and solutions,”arXiv preprint arXiv:2503.06149, 2025

    2025 arXiv

  22. [23]

    Don’t hallucinate, abstain: Identifying llm knowledge gaps via multi- llm collaboration,

    S. Feng, W. Shi, Y . Wang, W. Ding, V . Balachandran, and Y . Tsvetkov, “Don’t hallucinate, abstain: Identifying llm knowledge gaps via multi- llm collaboration,”arXiv preprint arXiv:2402.00367, 2024

    2024 arXiv

  23. [25]

    Learning to decode collaboratively with multiple language models,

    S. Z. Shen, H. Lang, B. Wang, Y . Kim, and D. Sontag, “Learning to decode collaboratively with multiple language models,”arXiv preprint arXiv:2403.03870, 2024

    2024 arXiv

  24. [26]

    Performance analysis on the applications of large language models: A case for elderly care,

    S. Wanget al., “Performance analysis on the applications of large language models: A case for elderly care,” in2024 IEEE International Conference on High Performance Computing and Communications (HPCC). IEEE, 2024, pp. 145–151

    2024

  25. [28]

    Levels of ai agents: From rules to large language models,

    Y . Huang, “Levels of ai agents: From rules to large language models,” arXiv preprint arXiv:2405.06643, 2024

    2024 arXiv

  26. [29]

    Fine- tuning and deploying large language models over edges: Issues and approaches,

    Y . Dong, H. Zhang, C. Li, S. Guo, V . Leung, and X. Hu, “Fine- tuning and deploying large language models over edges: Issues and approaches,”arXiv preprint arXiv:2408.10691, 2024

    2024 arXiv

  27. [30]

    A survey on the integration and optimization of large language models in edge computing envi- ronments,

    S. Bhardwaj, P. Singh, and M. K. Pandit, “A survey on the integration and optimization of large language models in edge computing envi- ronments,” in2024 16th International Conference on Computer and Automation Engineering (ICCAE). IEEE, 2024, pp. 168–172

    2024

  28. [32]

    Empowering large language models to edge intelligence: A survey of edge efficient llms and techniques,

    R. Wang, Z. Gao, L. Zhang, S. Yue, and Z. Gao, “Empowering large language models to edge intelligence: A survey of edge efficient llms and techniques,”Computer Science Review, vol. 57, p. 100755, 2025

    2025

  29. [34]

    Language models at the edge: A survey on techniques, challenges, and applica- tions,

    S. Hadish, V . Bojkovi ´c, M. Aloqaily, and M. Guizani, “Language models at the edge: A survey on techniques, challenges, and applica- tions,” in2024 2nd International Conference on Foundation and Large Language Models (FLLM). IEEE, 2024, pp. 262–271

    2024

  30. [38]

    What is the role of small models in the llm era: A survey,

    L. Chen and G. Varoquaux, “What is the role of small models in the llm era: A survey,”arXiv preprint arXiv:2409.06857, 2024

    2024 arXiv

  31. [40]

    Cost-effective online multi-llm selection with versatile reward models,

    X. Dai, J. Li, X. Liu, A. Yu, and J. Lui, “Cost-effective online multi-llm selection with versatile reward models,”arXiv preprint arXiv:2405.16587, 2024

    2024 arXiv

  32. [41]

    Enhancing supermarket robot interac- tion: an equitable multi-level llm conversational interface for handling diverse customer intents,

    C. Nandkumar and L. Peternel, “Enhancing supermarket robot interac- tion: an equitable multi-level llm conversational interface for handling diverse customer intents,”Frontiers in Robotics and AI, vol. 12, p. 1576348, 2025

    2025

  33. [42]

    A case study of scalable content annotation using multi-llm consensus and human review,

    M. Yuan, J. Chen, Z. Xing, G. Mohammadi, and A. Quigley, “A case study of scalable content annotation using multi-llm consensus and human review,”arXiv preprint arXiv:2503.17620, 2025

    2025 arXiv

  34. [43]

    Gamechat: Multi-llm dialogue for safe, agile, and socially optimal multi-agent navigation in constrained environments,

    V . Mahadevan, S. Zhang, and R. Chandra, “Gamechat: Multi-llm dialogue for safe, agile, and socially optimal multi-agent navigation in constrained environments,”arXiv preprint arXiv:2503.12333, 2025

    2025 arXiv

  35. [44]

    Modular pluralism: Pluralistic alignment via multi-llm collaboration,

    S. Feng, T. Sorensen, Y . Liu, J. Fisher, C. Y . Park, Y . Choi, and Y . Tsvetkov, “Modular pluralism: Pluralistic alignment via multi-llm collaboration,”arXiv preprint arXiv:2406.15951, 2024

    2024 arXiv

  36. [45]

    Socrasynth: Multi-llm reasoning with conditional statis- tics,

    E. Y . Chang, “Socrasynth: Multi-llm reasoning with conditional statis- tics,”arXiv preprint arXiv:2402.06634, 2024

    2024 arXiv

  37. [46]

    Multi-llm text summarization,

    J. Fang, C.-T. Liu, J. Kim, Y . Bhedaru, E. Liu, N. Singh, N. Lipka, P. Mathur, N. K. Ahmed, F. Dernoncourtet al., “Multi-llm text summarization,”arXiv preprint arXiv:2412.15487, 2024

    2024 arXiv

  38. [47]

    Mlsdet: Multi-llm statisti- cal deep ensemble for chinese ai-generated text detection,

    D. Mao, D. Zhang, A. Zhang, and Z. Zhao, “Mlsdet: Multi-llm statisti- cal deep ensemble for chinese ai-generated text detection,” inICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025, pp. 1–5

    2025

  39. [48]

    Improving the end-to- end efficiency of offline inference for multi-llm applications based on sampling and simulation,

    J. Fang, Y . Shen, Y . Wang, and L. Chen, “Improving the end-to- end efficiency of offline inference for multi-llm applications based on sampling and simulation,”arXiv preprint arXiv:2503.16893, 2025

    2025 arXiv

  40. [49]

    Multi-agent collaboration mechanisms: A survey of llms,

    K.-T. Tran, D. Dao, M.-D. Nguyen, Q.-V . Pham, B. O’Sullivan, and H. D. Nguyen, “Multi-agent collaboration mechanisms: A survey of llms,”arXiv preprint arXiv:2501.06322, 2025

    2025 arXiv

  41. [50]

    Minimizing hallucinations and communication costs: Adversarial debate and voting mechanisms in llm-based multi-agents,

    Y . Yang, Y . Ma, H. Feng, Y . Cheng, and Z. Han, “Minimizing hallucinations and communication costs: Adversarial debate and voting mechanisms in llm-based multi-agents,”Applied Sciences, vol. 15, no. 7, p. 3676, 2025

    2025

  42. [51]

    Adversarial multi-agent evaluation of large language models through iterative debates,

    C. Bandi and A. Harrasse, “Adversarial multi-agent evaluation of large language models through iterative debates,”arXiv preprint arXiv:2410.04663, 2024

    2024

  43. [52]

    Prediction of methane hydrate equilibrium in saline water so- lutions based on support vector machine and decision tree techniques,

    C.-Y . Hsu, J. S. Bu ˜nay Guaman, A. Ved, A. Yadav, G. Ezhilarasan, A. Rameshbabu, A. Alkhayyat, D. Aulakh, S. Choudhury, S. Sunori et al., “Prediction of methane hydrate equilibrium in saline water so- lutions based on support vector machine and decision tree techniques,” Sci...

    2025

  44. [53]

    An energy efficient ecg ventricular ectopic beat classifier using binarized cnn for edge ai devices,

    D. L. T. Wong, Y . Li, D. John, W. K. Ho, and C.-H. Heng, “An energy efficient ecg ventricular ectopic beat classifier using binarized cnn for edge ai devices,”IEEE Transactions on Biomedical Circuits and Systems, vol. 16, no. 2, pp. 222–232, 2022

    2022

  45. [54]

    Secure ai for 6g mobile devices: Deep learning optimization against side-channel attacks,

    A. A. Ahmed, M. K. Hasan, I. Memon, A. H. M. Aman, S. Islam, T. R. Gadekallu, and S. A. Memon, “Secure ai for 6g mobile devices: Deep learning optimization against side-channel attacks,”IEEE Transactions on Consumer Electronics, 2024

    2024

  46. [55]

    Privacy and artificial intelligence,

    J. Curzon, T. A. Kosa, R. Akalu, and K. El-Khatib, “Privacy and artificial intelligence,”IEEE Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 96–108, 2021

    2021

  47. [56]

    Data-centric artificial intelligence: A survey,

    D. Zha, Z. P. Bhat, K.-H. Lai, F. Yang, Z. Jiang, S. Zhong, and X. Hu, “Data-centric artificial intelligence: A survey,”ACM Computing Surveys, vol. 57, no. 5, pp. 1–42, 2025

    2025

  48. [57]

    On the reasoning capacity of ai models and how to quantify it,

    S. K. Radha and O. Goktas, “On the reasoning capacity of ai models and how to quantify it,”arXiv preprint arXiv:2501.13833, 2025

    2025 arXiv

  49. [58]

    The pipeline for the continuous development of artificial intelligence models—current state of research and practice,

    M. Steidl, M. Felderer, and R. Ramler, “The pipeline for the continuous development of artificial intelligence models—current state of research and practice,”Journal of Systems and Software, vol. 199, p. 111615, 2023

    2023

  50. [59]

    Edge ai: a survey,

    R. Singh and S. S. Gill, “Edge ai: a survey,”Internet of Things and Cyber-Physical Systems, vol. 3, pp. 71–92, 2023

    2023

  51. [60]

    Megalodon: Efficient llm pretraining and inference with unlimited context length,

    X. Ma, X. Yang, W. Xiong, B. Chen, L. Yu, H. Zhang, J. May, L. Zettlemoyer, O. Levy, and C. Zhou, “Megalodon: Efficient llm pretraining and inference with unlimited context length,”Advances in Neural Information Processing Systems, vol. 37, pp. 71 831–71 854, 2024

    2024

  52. [61]

    Large language models for networking: Applications, enabling techniques, and challenges,

    Y . Huang, H. Du, X. Zhang, D. Niyato, J. Kang, Z. Xiong, S. Wang, and T. Huang, “Large language models for networking: Applications, enabling techniques, and challenges,”IEEE Network, 2024

    2024

  53. [62]

    Gia: Llm-enabled generative intent abstraction to enhance adaptability for intent-driven networks,

    S. Kou, C. Yang, and M. Gurusamy, “Gia: Llm-enabled generative intent abstraction to enhance adaptability for intent-driven networks,” IEEE Transactions on Cognitive Communications and Networking, 2025

    2025

  54. [63]

    Generative ai for space-air-ground integrated networks,

    R. Zhang, H. Du, D. Niyato, J. Kang, Z. Xiong, A. Jamalipour, P. Zhang, and D. I. Kim, “Generative ai for space-air-ground integrated networks,”IEEE Wireless Communications, vol. 38, no. 4, pp. 10–20, 2024

    2024

  55. [64]

    A tutorial on llm reasoning: Relevant methods behind chatgpt o1,

    J. Wang, “A tutorial on llm reasoning: Relevant methods behind chatgpt o1,”arXiv preprint arXiv:2502.10867, 2025

    2025 arXiv

  56. [65]

    Large language model (llm) for telecommu- nications: A comprehensive survey on principles, key techniques, and opportunities,

    H. Zhou, C. Hu, Y . Yuan, Y . Cui, Y . Jin, C. Chen, H. Wu, D. Yuan, L. Jiang, D. Wuet al., “Large language model (llm) for telecommu- nications: A comprehensive survey on principles, key techniques, and opportunities,”IEEE Communications Surveys & Tutorials, 2024

    2024

  57. [66]

    Beyond the cloud: Edge inference for generative large language models in wireless networks,

    X. Zhang, J. Nie, Y . Huang, G. Xie, Z. Xiong, J. Liu, D. Niyato, and X. S. Shen, “Beyond the cloud: Edge inference for generative large language models in wireless networks,”IEEE Transactions on Wireless Communications, 2024

    2024

  58. [67]

    Generative ai agents with large language model for satellite networks via a mixture of experts transmission,

    R. Zhanget al., “Generative ai agents with large language model for satellite networks via a mixture of experts transmission,”IEEE Journal on Selected Areas in Communications, vol. 42, no. 12, pp. 3581–3596, 2024

    2024

  59. [68]

    Toward generalizable evaluation in the llm era: A survey beyond benchmarks,

    Y . Cao, S. Hong, X. Li, J. Ying, Y . Ma, H. Liang, Y . Liu, Z. Yao, X. Wang, D. Huanget al., “Toward generalizable evaluation in the llm era: A survey beyond benchmarks,”arXiv preprint arXiv:2504.18838, 2025

    2025 arXiv

  60. [69]

    Distill-vq: Learning retrieval oriented vector quantization by distilling knowledge from dense embeddings,

    S. Xiao, Z. Liu, W. Han, J. Zhang, D. Lian, Y . Gong, Q. Chen, F. Yang, H. Sun, Y . Shaoet al., “Distill-vq: Learning retrieval oriented vector quantization by distilling knowledge from dense embeddings,” inProceedings of the 45th International ACM SIGIR Conference on Research...

    2022

  61. [70]

    Software orchestrated and hardware accelerated artificial intelligence: Toward low latency edge computing,

    C. Deng, X. Fang, X. Wang, and K. Law, “Software orchestrated and hardware accelerated artificial intelligence: Toward low latency edge computing,”IEEE Wireless Communications, vol. 29, no. 4, pp. 110– 117, 2022

    2022

  62. [71]

    Splitwise: Efficient generative llm inference using phase splitting,

    P. Patel, E. Choukse, C. Zhang, A. Shah, ´I. Goiri, S. Maleki, and R. Bianchini, “Splitwise: Efficient generative llm inference using phase splitting,” in2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA). IEEE, 2024, pp. 118–132

    2024

  63. [72]

    Unibias: Unveiling and mitigating llm bias through internal attention and ffn manipula- tion,

    H. Zhou, Z. Feng, Z. Zhu, J. Qian, and K. Mao, “Unibias: Unveiling and mitigating llm bias through internal attention and ffn manipula- tion,”arXiv preprint arXiv:2405.20612, 2024

    2024 arXiv

  64. [73]

    Eazy: Eliminating hallucinations in lvlms by zeroing out hallucinatory image tokens,

    L. Che, T. Q. Liu, J. Jia, W. Qin, R. Tang, and V . Pavlovic, “Eazy: Eliminating hallucinations in lvlms by zeroing out hallucinatory image tokens,”arXiv preprint arXiv:2503.07772, 2025

    2025 arXiv

  65. [74]

    From calculation to adjudication: Examining llm judges on mathematical reasoning tasks,

    A. Stephan, D. Zhu, M. Aßenmacher, X. Shen, and B. Roth, “From calculation to adjudication: Examining llm judges on mathematical reasoning tasks,”arXiv preprint arXiv:2409.04168, 2024

    2024 arXiv

  66. [75]

    Generating efficient training data via llm-based attribute manipulation,

    L. Peng, Y . Zhang, and J. Shang, “Generating efficient training data via llm-based attribute manipulation,”arXiv preprint arXiv:2307.07099, 2023

    2023 arXiv

  67. [76]

    Trism for agentic ai: A review of trust, risk, and security management in llm- based agentic multi-agent systems,

    S. Raza, R. Sapkota, M. Karkee, and C. Emmanouilidis, “Trism for agentic ai: A review of trust, risk, and security management in llm- based agentic multi-agent systems,”arXiv preprint arXiv:2506.04133, 2025

    2025

  68. [77]

    Learning in chaos: Efficient autoscaling and self-healing for distributed training at the edge,

    W. Feng, R. Xiao, Z. Li, H. Yu, G. Sun, L. Luo, M. Guizani, and Q. Ho, “Learning in chaos: Efficient autoscaling and self-healing for distributed training at the edge,”arXiv preprint arXiv:2505.12815, 2025

    2025 arXiv

  69. [78]

    A scalable communication protocol for networks of large language models,

    S. Marro, E. La Malfa, J. Wright, G. Li, N. Shadbolt, M. Wooldridge, and P. Torr, “A scalable communication protocol for networks of large language models,”arXiv preprint arXiv:2410.11905, 2024. 21

    2024 arXiv

  70. [79]

    Edgeshard: Efficient llm inference via collaborative edge computing,

    M. Zhang, X. Shen, J. Cao, Z. Cui, and S. Jiang, “Edgeshard: Efficient llm inference via collaborative edge computing,”IEEE Internet of Things Journal, vol. 12, no. 10, pp. 13 119–13 131, 2025

    2025

  71. [80]

    Eco-llm: Llm-based edge cloud optimization,

    K. Rao, G. Coviello, P. Benedetti, C. Giuseppe De Vita, G. Mellone, and S. Chakradhar, “Eco-llm: Llm-based edge cloud optimization,” in Proceedings of the 2024 Workshop on AI For Systems, 2024, pp. 7–12

    2024

  72. [81]

    Multi-agent ai system for adaptive cognitive training in elderly care,

    I. Ferri-Molla, J. Linares-Pellicer, C. Aliaga-Torro, and J. Izquierdo- Domenech, “Multi-agent ai system for adaptive cognitive training in elderly care,” inProceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART), 2025, pp. 937–947

    2025

  73. [82]

    A comprehen- sive survey on artificial intelligence empowered edge computing on consumer electronics,

    J.-H. Syu, J. C.-W. Lin, G. Srivastava, and K. Yu, “A comprehen- sive survey on artificial intelligence empowered edge computing on consumer electronics,”IEEE Transactions on Consumer Electronics, vol. 69, no. 4, pp. 1023–1034, 2023

    2023

  74. [83]

    An internet-of-medical-things- enabled edge computing framework for tackling covid-19,

    M. A. Rahman and M. S. Hossain, “An internet-of-medical-things- enabled edge computing framework for tackling covid-19,”IEEE Internet of Things Journal, vol. 8, no. 21, pp. 15 847–15 854, 2021

    2021

  75. [84]

    A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom,

    T. Shaik, X. Tao, L. Li, H. Xie, and J. D. Vel ´asquez, “A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom,”Information Fusion, vol. 102, p. 102040, 2024

    2024

  76. [85]

    A privacy-preserving social computing framework for health management using federated learning,

    Z. Shen, F. Ding, Y . Yao, A. Bhardwaj, Z. Guo, and K. Yu, “A privacy-preserving social computing framework for health management using federated learning,”IEEE Transactions on Computational Social Systems, vol. 10, no. 4, pp. 1666–1678, 2022

    2022

  77. [86]

    Llmcad: Fast and scalable on-device large language model inference,

    D. Xu, W. Yin, X. Jin, Y . Zhang, S. Wei, M. Xu, and X. Liu, “Llmcad: Fast and scalable on-device large language model inference,”arXiv preprint arXiv:2309.04255, 2023

    2023 arXiv

  78. [87]

    Chatgpt and other large language models for cybersecurity of smart grid applications,

    A. Zaboli, S. L. Choi, T.-J. Song, and J. Hong, “Chatgpt and other large language models for cybersecurity of smart grid applications,” in2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024, pp. 1–5

    2024

  79. [88]

    Review of the opportunities and challenges to accelerate mass-scale application of smart grids with large-language models,

    H. Shi, L. Fang, X. Chen, C. Gu, K. Ma, X. Zhang, Z. Zhang, J. Gu, and E. G. Lim, “Review of the opportunities and challenges to accelerate mass-scale application of smart grids with large-language models,”IET Smart Grid, vol. 7, no. 6, pp. 737–759, 2024

    2024

  80. [89]

    Large language models integration in smart grids,

    S. Madani, A. Tavasoli, Z. K. Astaneh, and P.-O. Pineau, “Large language models integration in smart grids,”arXiv preprint arXiv:2504.09059, 2025

    2025 arXiv

  81. [90]

    Joint optimization of computing offloading and service caching in edge computing-based smart grid,

    H. Zhou, Z. Zhang, D. Li, and Z. Su, “Joint optimization of computing offloading and service caching in edge computing-based smart grid,” IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 1122–1132, 2022

    2022

  82. [91]

    Boosting 5g on smart grid communi- cation: A smart ran slicing approach,

    D. Carrillo, C. Kalalas, P. Raussi, D. S. Michalopoulos, D. Z. Rodr´ıguez, H. Kokkoniemi-Tarkkanen, K. Ahola, P. H. Nardelli, G. Fraidenraich, and P. Popovski, “Boosting 5g on smart grid communi- cation: A smart ran slicing approach,”IEEE Wireless Communications, vol. 30, no. ...

    2022

  83. [92]

    Domain specialization as the key to make large language models disruptive: A comprehensive survey,

    C. Ling, X. Zhao, J. Lu, C. Deng, C. Zheng, J. Wang, T. Chowdhury, Y . Li, H. Cui, X. Zhanget al., “Domain specialization as the key to make large language models disruptive: A comprehensive survey,” arXiv preprint arXiv:2305.18703, 2023

    2023 arXiv

  84. [93]

    The universal fog proxy: A third-party authentication solution for federated fog systems with multiple protocols,

    A. Ali, A. U. S ¸ahin, ¨O. ¨Ozkasap, and Y .-D. Lin, “The universal fog proxy: A third-party authentication solution for federated fog systems with multiple protocols,”IEEE Network, vol. 35, no. 6, pp. 285–291, 2022

    2022

  85. [94]

    H. Xu, J. Yuan, A. Zhou, G. Xu, W. Li, X. Ban, and X. Ye, “Genai- powered multi-agent paradigm for smart urban mobility: Opportu- nities and challenges for integrating large language models (llms) and retrieval-augmented generation (rag) with intelligent transportation systems...

    2024 arXiv

  86. [95]

    Integrating llms with its: Recent advances, potentials, challenges, and future directions,

    D. Mahmud, H. Hajmohamed, S. Almentheri, S. Alqaydi, L. Aldhaheri, R. A. Khalil, and N. Saeed, “Integrating llms with its: Recent advances, potentials, challenges, and future directions,”IEEE Transactions on Intelligent Transportation Systems, 2025

    2025

  87. [96]

    Federated learning assisted intelligent iov mobile edge computing,

    H. Quan, Q. Zhang, and J. Zhao, “Federated learning assisted intelligent iov mobile edge computing,”IEEE Transactions on Green Communi- cations and Networking, 2024

    2024

  88. [97]

    Potential game based distributed iov service offloading with graph attention networks in mobile edge computing,

    Q. Jiang, X. Xu, M. Bilal, J. Crowcroft, Q. Liu, W. Dou, and J. Jiang, “Potential game based distributed iov service offloading with graph attention networks in mobile edge computing,”IEEE Transactions on Intelligent Transportation Systems, 2024

    2024

  89. [98]

    Esia: An efficient and stable identity authentication for internet of vehicles,

    H. Luo, J. Zhang, X. Li, Z. Li, H. Yu, G. Sun, and D. Niyato, “Esia: An efficient and stable identity authentication for internet of vehicles,” IEEE Transactions on Vehicular Technology, vol. 73, no. 4, pp. 5602– 5615, 2024

    2024

  90. [99]

    Multi- agent reinforcement learning based edge content caching for connected autonomous vehicles in iov,

    X. Xu, L. Gu, M. Bilal, M. Khan, Y . Wen, G. Liu, and Y . Yuan, “Multi- agent reinforcement learning based edge content caching for connected autonomous vehicles in iov,”ACM Transactions on Autonomous and Adaptive Systems, 2024

    2024

  91. [100]

    Em- bodied artificial intelligence-enabled internet of vehicles: Challenges and solutions,

    M. Chen, C. Wang, X. He, F. Zhu, L. Wang, and A. V . Vasilakos, “Em- bodied artificial intelligence-enabled internet of vehicles: Challenges and solutions,”IEEE Vehicular Technology Magazine, 2025

    2025

  92. [101]

    Secure physical layer communica- tions for low-altitude economy networking: A survey,

    L. Cai, J. Wang, R. Zhang, Y . Zhang, T. Jiang, D. Niyato, X. Wang, A. Jamalipour, and X. Shen, “Secure physical layer communica- tions for low-altitude economy networking: A survey,”arXiv preprint arXiv:2504.09153, 2025

    2025 arXiv

  93. [102]

    Toward realization of low-altitude economy networks: Core architecture, integrated technologies, and future direc- tions,

    Y . Wang, G. Sun, Z. Sun, J. Wang, J. Li, C. Zhao, J. Wu, S. Liang, M. Yin, P. Wanget al., “Toward realization of low-altitude economy networks: Core architecture, integrated technologies, and future direc- tions,”arXiv preprint arXiv:2504.21583, 2025

    2025 arXiv

  94. [103]

    Generative ai-enabled wireless communi- cations for robust low-altitude economy networking,

    C. Zhao, J. Wang, R. Zhang, D. Niyato, G. Sun, H. Du, D. I. Kim, and A. Jamalipour, “Generative ai-enabled wireless communi- cations for robust low-altitude economy networking,”arXiv preprint arXiv:2502.18118, 2025

    2025 arXiv

  95. [104]

    Task offloading with llm-enhanced multi-agent reinforcement learning in uav-assisted edge computing,

    F. Zhu, F. Huang, Y . Yu, G. Liu, and T. Huang, “Task offloading with llm-enhanced multi-agent reinforcement learning in uav-assisted edge computing,”Sensors, vol. 25, no. 1, p. 175, 2024

    2024

  96. [105]

    Large language model-enhanced reinforcement learning for low-altitude economy networking,

    L. Cai, R. Zhang, C. Zhao, Y . Zhang, J. Kang, D. Niyato, T. Jiang, and X. Shen, “Large language model-enhanced reinforcement learning for low-altitude economy networking,”arXiv preprint arXiv:2505.21045, 2025

    2025 arXiv

  97. [106]

    A survey on the convergence of edge computing and ai for uavs: Opportunities and challenges,

    P. McEnroe, S. Wang, and M. Liyanage, “A survey on the convergence of edge computing and ai for uavs: Opportunities and challenges,”IEEE Internet of Things Journal, vol. 9, no. 17, pp. 15 435–15 459, 2022

    2022

  98. [107]

    Escm: An effi- cient and secure communication mechanism for uav networks,

    H. Luo, Y . Wu, G. Sun, H. Yu, and M. Guizani, “Escm: An effi- cient and secure communication mechanism for uav networks,”IEEE Transactions on Network and Service Management, vol. 21, no. 3, pp. 3124–3139, 2024

    2024

  99. [108]

    Split-chain based efficient blockchain-assisted cross-domain authentication for iot,

    D. Luo, Q. Cai, G. Sun, H. Yu, and D. Niyato, “Split-chain based efficient blockchain-assisted cross-domain authentication for iot,”IEEE Transactions on Network and Service Management, 2024

    2024

  100. [109]

    Temporal spectrum cartography in low-altitude economy networks: A generative ai framework with multi- agent learning,

    C. Zhao, R. Zhang, J. Wang, D. Niyato, G. Sun, H. Du, Z. Li, A. Jamalipour, and D. I. Kim, “Temporal spectrum cartography in low-altitude economy networks: A generative ai framework with multi- agent learning,”arXiv preprint arXiv:2505.15571, 2025

    2025 arXiv

  101. [110]

    Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,

    V . Sanh, L. Debut, J. Chaumond, and T. Wolf, “Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,”arXiv preprint arXiv:1910.01108, 2019

    1910 arXiv

  102. [111]

    Tinybert: Distilling bert for natural language understanding,

    X. Jiao, Y . Yin, L. Shang, X. Jiang, X. Chen, L. Li, F. Wang, and Q. Liu, “Tinybert: Distilling bert for natural language understanding,” arXiv preprint arXiv:1909.10351, 2019

    1909 arXiv

  103. [112]

    Low-resource mobilebert for emotion recognition in imbalanced text datasets mitigating challenges with limited resources,

    M. Hussain, C. Chen, S. S. Albouq, K. Shinan, F. Alanazi, M. W. Iqbal, and M. U. Ashraf, “Low-resource mobilebert for emotion recognition in imbalanced text datasets mitigating challenges with limited resources,” PloS one, vol. 20, no. 1, p. e0312867, 2025

    2025

  104. [113]

    Albert: A lite bert for self-supervised learning of language represen- tations,

    Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “Albert: A lite bert for self-supervised learning of language represen- tations,”arXiv preprint arXiv:1909.11942, 2019

    1909 arXiv

  105. [114]

    Edgebert: Sentence-level energy optimizations for latency-aware multi-task nlp inference,

    T. Tambe, C. Hooper, L. Pentecost, T. Jia, E.-Y . Yang, M. Donato, V . Sanh, P. Whatmough, A. M. Rush, D. Brookset al., “Edgebert: Sentence-level energy optimizations for latency-aware multi-task nlp inference,” inMICRO-54: 54th Annual IEEE/ACM International Sym- posium on Mic...

    2021

  106. [115]

    Cost-saving llm cascades with early abstention,

    M. J. Zellinger, R. Liu, and M. Thomson, “Cost-saving llm cascades with early abstention,”arXiv preprint arXiv:2502.09054, 2025

    2025 arXiv

  107. [116]

    Large-scale traffic flow forecast with lightweight llm in edge intelligence,

    Y . Rong, Y . Mao, X. He, and M. Chen, “Large-scale traffic flow forecast with lightweight llm in edge intelligence,”IEEE Internet of Things Magazine, vol. 8, no. 1, pp. 12–18, 2025

    2025

  108. [117]

    Split learning in 6g edge networks,

    Z. Lin, G. Qu, X. Chen, and K. Huang, “Split learning in 6g edge networks,”IEEE Wireless Communications, vol. 31, no. 4, pp. 170– 176, 2024

    2024

  109. [118]

    Ce-collm: Efficient and adaptive large lan- guage models through cloud-edge collaboration,

    H. Jin and Y . Wu, “Ce-collm: Efficient and adaptive large lan- guage models through cloud-edge collaboration,”arXiv preprint arXiv:2411.02829, 2024

    2024 arXiv

  110. [119]

    Deadline-aware online job scheduling for distributed training in heterogeneous clusters,

    Y . Zhang, L. Luo, G. Sun, H. Yu, and B. Li, “Deadline-aware online job scheduling for distributed training in heterogeneous clusters,”IEEE Transactions on Cloud Computing, 2025

    2025

  111. [120]

    Slicing- based artificial intelligence service provisioning on the network edge: Balancing ai service performance and resource consumption of data management,

    M. Li, J. Gao, C. Zhou, X. S. Shen, and W. Zhuang, “Slicing- based artificial intelligence service provisioning on the network edge: Balancing ai service performance and resource consumption of data management,”IEEE Vehicular Technology Magazine, vol. 16, no. 4, pp. 16–26, 2021. 22

    2021

  112. [121]

    Hybrid llm: Cost-efficient and quality-aware query routing,

    D. Ding, A. Mallick, C. Wang, R. Sim, S. Mukherjee, V . Ruhle, L. V . Lakshmanan, and A. H. Awadallah, “Hybrid llm: Cost-efficient and quality-aware query routing,”arXiv preprint arXiv:2404.14618, 2024

    2024 arXiv

  113. [122]

    Agreement-based cascading for efficient inference,

    S. Kolawole, D. Dennis, A. Talwalkar, and V . Smith, “Agreement-based cascading for efficient inference,”arXiv preprint arXiv:2407.02348, 2024

    2024

  114. [123]

    Multi-agent deep reinforcement learning-based inference task scheduling and offloading for maximum inference accuracy under time and energy constraints,

    A. Ben Sada, A. Khelloufi, A. Naouri, H. Ning, N. Aung, and S. Dhelim, “Multi-agent deep reinforcement learning-based inference task scheduling and offloading for maximum inference accuracy under time and energy constraints,”Electronics, vol. 13, no. 13, p. 2580, 2024

    2024

  115. [124]

    A survey on model context protocol: Architecture, state-of- the-art, challenges and future directions,

    P. P. Ray, “A survey on model context protocol: Architecture, state-of- the-art, challenges and future directions,”Authorea Preprints, 2025

    2025

  116. [125]

    In-context autoencoder for context compression in a large language model,

    T. Ge, J. Hu, L. Wang, X. Wang, S.-Q. Chen, and F. Wei, “In-context autoencoder for context compression in a large language model,” in International Conference on Learning Representations (ICLR), 2024

    2024

  117. [126]

    Edgeinfinite: A memory-efficient infinite-context transformer for edge devices,

    J. Chen, S. Peng, D. Luo, F. Yang, R. Wu, F. Li, and X. Chen, “Edgeinfinite: A memory-efficient infinite-context transformer for edge devices,”arXiv preprint arXiv:2503.22196, 2025

    2025 arXiv

  118. [127]

    Model-distributed in- ference for large language models at the edge,

    D. Macario, H. Seferoglu, and E. Koyuncu, “Model-distributed in- ference for large language models at the edge,”arXiv preprint arXiv:2505.18164, 2025

    2025 arXiv

  119. [128]

    Collabo- rative memory: Multi-user memory sharing in llm agents with dynamic access control,

    A. Rezazadeh, Z. Li, A. Lou, Y . Zhao, W. Wei, and Y . Bao, “Collabo- rative memory: Multi-user memory sharing in llm agents with dynamic access control,”arXiv preprint arXiv:2505.18279, 2025

    2025 arXiv

  120. [129]

    Memory sharing for large language model based agents,

    H. Gao and Y . Zhang, “Memory sharing for large language model based agents,”arXiv preprint arXiv:2404.09982, 2024

    2024

  121. [130]

    Distributed mixture-of-agents for edge inference with large language models,

    P. Mitra, P. Kaswan, and S. Ulukus, “Distributed mixture-of-agents for edge inference with large language models,”arXiv preprint arXiv:2412.21200, 2024

    2024 arXiv

  122. [131]

    Rescriber: Smaller-llm-powered user-led data minimization for llm-based chatbots,

    J. Zhou, E. Xu, Y . Wu, and T. Li, “Rescriber: Smaller-llm-powered user-led data minimization for llm-based chatbots,” inProceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 2025, pp. 1–28

    2025

  123. [132]

    On protecting the data privacy of large language models (llms): A survey,

    B. Yan, K. Li, M. Xu, Y . Dong, Y . Zhang, Z. Ren, and X. Cheng, “On protecting the data privacy of large language models (llms): A survey,” arXiv preprint arXiv:2403.05156, 2024

    2024 arXiv

  124. [133]

    Teeslice: Protecting sensitive neural network models in trusted execution envi- ronments when attackers have pre-trained models,

    D. Li, Z. Zhang, M. Yao, Y . Cai, Y . Guo, and X. Chen, “Teeslice: Protecting sensitive neural network models in trusted execution envi- ronments when attackers have pre-trained models,”ACM Transactions on Software Engineering and Methodology, 2024

    2024

  125. [134]

    Split-and-denoise: Protect large language model inference with local differential privacy,

    P. Mai, R. Yan, Z. Huang, Y . Yang, and Y . Pang, “Split-and-denoise: Protect large language model inference with local differential privacy,” arXiv preprint arXiv:2310.09130, 2023

    2023 arXiv

  126. [135]

    Fedbiot: Llm local fine- tuning in federated learning without full model,

    F. Wu, Z. Li, Y . Li, B. Ding, and J. Gao, “Fedbiot: Llm local fine- tuning in federated learning without full model,” inProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, pp. 3345–3355

    2024

  127. [136]

    Optimizing federated learning in distributed industrial iot: A multi- agent approach,

    W. Zhang, D. Yang, W. Wu, H. Peng, N. Zhang, H. Zhang, and X. Shen, “Optimizing federated learning in distributed industrial iot: A multi- agent approach,”IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, pp. 3688–3703, 2021

    2021

  128. [137]

    Confidential prompting: Protecting user prompts from cloud llm providers,

    I. Gim, C. Li, and L. Zhong, “Confidential prompting: Protecting user prompts from cloud llm providers,”arXiv preprint arXiv:2409.19134, 2024

    2024

  129. [138]

    Generating privacy-preserving per- sonalized advice with zero-knowledge proofs and llms,

    H. Watanabe and M. Uchikoshi, “Generating privacy-preserving per- sonalized advice with zero-knowledge proofs and llms,” inCompanion Proceedings of the ACM on Web Conference 2025, 2025, pp. 1385– 1389

    2025

  130. [139]

    Privacy-enhancing paradigms within federated multi-agent systems,

    Z. Shi, G. Wan, W. Huang, G. Zhang, J. Shao, M. Ye, and C. Yang, “Privacy-enhancing paradigms within federated multi-agent systems,” arXiv preprint arXiv:2503.08175, 2025

    2025 arXiv

  131. [140]

    Memory-efficient split federated learning for llm fine-tuning on heterogeneous mobile devices,

    X. Chen, L. Li, F. Ji, and W. Wen, “Memory-efficient split federated learning for llm fine-tuning on heterogeneous mobile devices,”arXiv preprint arXiv:2506.02940, 2025

    2025 arXiv

  132. [141]

    Edge-llm: Enabling efficient large language model adaptation on edge devices via unified compression and adaptive layer voting,

    Z. Yu, Z. Wang, Y . Li, R. Gao, X. Zhou, S. R. Bommu, Y . Zhao, and Y . Lin, “Edge-llm: Enabling efficient large language model adaptation on edge devices via unified compression and adaptive layer voting,” inProceedings of the 61st ACM/IEEE Design Automation Conference, 2024, pp. 1–6

    2024

  133. [142]

    Towards building the federatedgpt: Federated instruction tuning,

    J. Zhang, S. Vahidian, M. Kuo, C. Li, R. Zhang, T. Yu, G. Wang, and Y . Chen, “Towards building the federatedgpt: Federated instruction tuning,” inICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp. 6915–6919

    2024

  134. [143]

    Split fine-tuning for large language models in wireless networks,

    S. Zhang, G. Cheng, X. Huang, Z. Li, W. Wu, L. Song, and X. Shen, “Split fine-tuning for large language models in wireless networks,” IEEE Journal of Selected Topics in Signal Processing, 2025

    2025

  135. [144]

    Splitlora: A split parameter-efficient fine-tuning framework for large language models,

    Z. Lin, X. Hu, Y . Zhang, Z. Chen, Z. Fang, X. Chen, A. Li, P. Vepakomma, and Y . Gao, “Splitlora: A split parameter-efficient fine-tuning framework for large language models,”arXiv preprint arXiv:2407.00952, 2024

    2024 arXiv

  136. [145]

    Multimodal federated learning: A survey,

    L. Che, J. Wang, Y . Zhou, and F. Ma, “Multimodal federated learning: A survey,”Sensors, vol. 23, no. 15, p. 6986, 2023

    2023

  137. [146]

    Leveraging foundation models for multi-modal federated learning with incomplete modality,

    L. Che, J. Wang, X. Liu, and F. Ma, “Leveraging foundation models for multi-modal federated learning with incomplete modality,” inJoint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2024, pp. 401–417

    2024

  138. [147]

    Llm-fusion: A novel multimodal fusion model for accelerated material discovery,

    O. Boyar, I. Priyadarsini, S. Takeda, and L. Hamada, “Llm-fusion: A novel multimodal fusion model for accelerated material discovery,” arXiv preprint arXiv:2503.01022, 2025

    2025 arXiv

  139. [148]

    Research on multimodal data fusion technology based on llm and attention mechanism,

    B. You, “Research on multimodal data fusion technology based on llm and attention mechanism,” in2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA), 2025, pp. 1–4

    2025

  140. [149]

    Multimodal alignment and fusion: A survey,

    S. Li and H. Tang, “Multimodal alignment and fusion: A survey,”arXiv preprint arXiv:2411.17040, 2024

    2024

  141. [150]

    Qwen-vl: A versatile vision-language model for un- derstanding, localization, text reading, and beyond,

    J. Bai, S. Bai, S. Yang, S. Wang, S. Tan, P. Wang, J. Lin, C. Zhou, and J. Zhou, “Qwen-vl: A versatile vision-language model for un- derstanding, localization, text reading, and beyond,”arXiv preprint arXiv:2308.12966, 2023

    2023 arXiv

  142. [151]

    Att-sinkhorn: Multimodal alignment with sinkhorn-based deep attention architecture,

    Q. Ma, M. Zhang, Y . Tang, and Z. Huang, “Att-sinkhorn: Multimodal alignment with sinkhorn-based deep attention architecture,” in2023 28th International Conference on Automation and Computing (ICAC). IEEE, 2023, pp. 1–6

    2023

  143. [152]

    WormGPT: A large language model chatbot for criminals,

    M. F. M. Firdhouset al., “WormGPT: A large language model chatbot for criminals,” in2023 24th International Arab Conference on Information Technology (ACIT). IEEE, 2023, pp. 1–6

    2023

  144. [153]

    An energy-efficient wireless blockchain sharding scheme for pbft consensus,

    H. Luo, G. Sun, H. Yu, B. Lei, and M. Guizani, “An energy-efficient wireless blockchain sharding scheme for pbft consensus,”IEEE Trans- actions on Network Science and Engineering, vol. 11, no. 3, pp. 3015– 3027, 2024

    2024

  145. [154]

    Uls-pbft: An ultra-low storage overhead pbft consensus for blockchain,

    H. Luo, “Uls-pbft: An ultra-low storage overhead pbft consensus for blockchain,”Blockchain: Research and Applications, vol. 4, no. 4, p. 100155, 2023

    2023

  146. [155]

    Drdst: Low-latency dag consensus through robust dynamic sharding and tree- broadcasting for iov,

    R. Chen, H. Luo, G. Sun, H. Yu, D. Niyato, and S. Dustdar, “Drdst: Low-latency dag consensus through robust dynamic sharding and tree- broadcasting for iov,”arXiv preprint arXiv:2412.04742, 2024

    2024 arXiv

  147. [156]

    Convergence of sym- biotic communications and blockchain for sustainable and trustworthy 6g wireless networks,

    H. Luo, G. Sun, C. Chi, H. Yu, and M. Guizani, “Convergence of sym- biotic communications and blockchain for sustainable and trustworthy 6g wireless networks,”IEEE Wireless Communications, vol. 32, no. 2, pp. 18–25, 2025

    2025

  148. [157]

    Blockchain-empowered lifecycle management for ai-generated content products in edge networks,

    Y . Liu, H. Du, D. Niyato, J. Kang, Z. Xiong, C. Miao, X. Shen, and A. Jamalipour, “Blockchain-empowered lifecycle management for ai-generated content products in edge networks,”IEEE Wireless Communications, vol. 31, no. 3, pp. 286–294, 2024

    2024

  149. [158]

    Prosecutor: Protecting mobile aigc services on two-layer blockchain via reputation and contract theoretic approaches,

    Y . Liu, H. Du, D. Niyato, J. Kang, Z. Xiong, A. Jamalipour, and X. Shen, “Prosecutor: Protecting mobile aigc services on two-layer blockchain via reputation and contract theoretic approaches,”IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 10 966–10 983, 2024

    2024

  150. [159]

    Accelerating block and transaction propagation: A survey on broadcast protocols in blockchain networks,

    Y . Lai, Y . Liu, H. Luo, G. Sun, C. Chi, and H. Yu, “Accelerating block and transaction propagation: A survey on broadcast protocols in blockchain networks,”IEEE Transactions on Network Science and Engineering, 2025

    2025

  151. [160]

    Blockchain for energy market: A comprehensive survey,

    T. Jiang, H. Luo, K. Yang, G. Sun, H. Yu, Q. Huang, and A. V . Vasilakos, “Blockchain for energy market: A comprehensive survey,” Sustainable Energy, Grids and Networks, p. 101614, 2024

    2024

  152. [161]

    Wireless blockchain meets 6g: The future trustworthy and ubiquitous connectivity,

    H. Luo, G. Sun, J. Wang, H. Yu, D. Niyato, S. Dustdar, and Z. Han, “Wireless blockchain meets 6g: The future trustworthy and ubiquitous connectivity,”Authorea Preprints, 2025

    2025

  153. [162]

    Symbiotic blockchain consensus: Cognitive backscatter communications-enabled wireless blockchain consensus,

    H. Luoet al., “Symbiotic blockchain consensus: Cognitive backscatter communications-enabled wireless blockchain consensus,”IEEE/ACM Transactions on Networking, vol. 32, no. 6, pp. 5372–5387, 2024

    2024

  154. [163]

    A blockchain-enabled cold start aggregation scheme for federated reinforcement learning-based task offloading in zero trust leo satellite networks,

    B. Mao, Y . Liu, Z. Wei, H. Guo, Y . Xun, J. Wang, J. Liu, and N. Kato, “A blockchain-enabled cold start aggregation scheme for federated reinforcement learning-based task offloading in zero trust leo satellite networks,”IEEE Journal on Selected Areas in Communications, 2025

    2025

  155. [164]

    Fspo: Few-shot preference optimization of synthetic preference data in llms elicits effective personalization to real users,

    A. Singh, S. Hsu, K. Hsu, E. Mitchell, S. Ermon, T. Hashimoto, A. Sharma, and C. Finn, “Fspo: Few-shot preference optimization of synthetic preference data in llms elicits effective personalization to real users,”arXiv preprint arXiv:2502.19312, 2025. 23

    2025 arXiv

  156. [165]

    Uncertainty-based learning of a lightweight model for multimodal emotion recognition,

    A. Radoi and G. Cioroiu, “Uncertainty-based learning of a lightweight model for multimodal emotion recognition,”IEEE Access, vol. 12, pp. 120 362–120 374, 2024

    2024

  157. [166]

    Thin mobilenet: An enhanced mo- bilenet architecture,

    D. Sinha and M. El-Sharkawy, “Thin mobilenet: An enhanced mo- bilenet architecture,” in2019 IEEE 10th annual ubiquitous computing, electronics & mobile communication conference (UEMCON). IEEE, 2019, pp. 0280–0285

    2019

  158. [167]

    M3bat: Unsu- pervised domain adaptation for multimodal mobile sensing with multi- branch adversarial training,

    L. Meegahapola, H. Hassoune, and D. Gatica-Perez, “M3bat: Unsu- pervised domain adaptation for multimodal mobile sensing with multi- branch adversarial training,”Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 8, no. 2, pp. 1–30, 2024

    2024

  159. [168]

    Large language model (llm)-enabled graphs in dynamic networking,

    G. Sun, Y . Wang, D. Niyato, J. Wang, X. Wang, H. V . Poor, and K. B. Letaief, “Large language model (llm)-enabled graphs in dynamic networking,”IEEE Network, 2024

    2024

  160. [169]

    Movable antenna enhanced federated fine-tuning of large language models via hybrid client selection optimization,

    Y . Zhao, Y . Xiu, C. Dai, N. Wei, and D. Niyato, “Movable antenna enhanced federated fine-tuning of large language models via hybrid client selection optimization,”arXiv preprint arXiv:2506.00011, 2025

    2025

  161. [170]

    Ai-c2c (conscious to conscience): a governance framework for ethical ai integration,

    T. Anthuvan and K. Maheshwari, “Ai-c2c (conscious to conscience): a governance framework for ethical ai integration,”AI and Ethics, pp. 1–13, 2025

    2025

  162. [171]

    Covert prompt transmission for secure large language model services,

    R. Zhang, Y . Liu, S. Tang, J. Wang, D. Niyato, G. Sun, Y . Li, and S. Sun, “Covert prompt transmission for secure large language model services,”arXiv preprint arXiv:2504.21311, 2025

    2025 arXiv

  163. [172]

    Retrieval-augmented perception: High-resolution image perception meets visual rag,

    W. Wang, Y . Jing, L. Ding, Y . Wang, L. Shen, Y . Luo, B. Du, and D. Tao, “Retrieval-augmented perception: High-resolution image perception meets visual rag,”arXiv preprint arXiv:2503.01222, 2025

    2025 arXiv

  164. [173]

    Interactive ai with retrieval-augmented generation for next generation networking,

    R. Zhang, H. Du, Y . Liu, D. Niyato, J. Kang, S. Sun, X. Shen, and H. V . Poor, “Interactive ai with retrieval-augmented generation for next generation networking,”IEEE Network, 2024

    2024

  165. [174]

    Chain-of-thought for large lan- guage model-empowered wireless communications,

    X. Wang, J. Zhu, R. Zhang, L. Feng, D. Niyato, J. Wang, H. Du, S. Mao, and Z. Han, “Chain-of-thought for large lan- guage model-empowered wireless communications,”arXiv preprint arXiv:2505.22320, 2025

    2025 arXiv

  166. [175]

    World models for cognitive agents: Transforming edge intelligence in future networks,

    C. Zhao, R. Zhang, J. Wang, G. Zhao, D. Niyato, G. Sun, S. Mao, and D. I. Kim, “World models for cognitive agents: Transforming edge intelligence in future networks,”arXiv preprint arXiv:2506.00417, 2025

    2025 arXiv

  167. [176]

    Wireless agentic ai with retrieval-augmented multimodal semantic perception,

    G. Liu, Y . Liu, R. Zhang, H. Du, D. Niyato, Z. Xiong, S. Sun, and A. Jamalipour, “Wireless agentic ai with retrieval-augmented multimodal semantic perception,”arXiv preprint arXiv:2505.23275, 2025

    2025 arXiv

  168. [177]

    Optimizing generative ai networking: A dual perspective with multi-agent systems and mixture of experts,

    R. Zhang, H. Du, D. Niyato, J. Kang, Z. Xiong, P. Zhang, and D. I. Kim, “Optimizing generative ai networking: A dual perspective with multi-agent systems and mixture of experts,”arXiv preprint arXiv:2405.12472, 2024

    2024 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.