Pith. sign in

REVIEW 3 major objections 5 minor 174 references

Artificial Intelligence for UAV-enabled Wireless Networks: A Survey

T0 review · 3 major / 5 minor · reviewed 2026-08-27 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This survey claims that research on AI-enabled UAV wireless networks is best organized by AI subfield—supervised/unsupervised machine learning, reinforcement learning, and federated learning—and that this structure exposes gaps, including…

desk verdict Useful survey map of AI for UAV networks; FL and IRS coverage are the real additions, but tutorial slips and duplicate references need fixing before it's citable as-is. read the letter →

arxiv 2009.11522 v2 pith:TU2WTJQI submitted 2020-09-24 eess.SP cs.ITcs.LGmath.IT

Mohamed-Amine Lahmeri , Mustafa A.Kishk , Mohamed-Slim Alouini This is my paper · ORCID
classification eess.SPcs.ITcs.LGmath.IT
keywords artificialintelligenceunmannedaerialvehiclesmachinelearningreinforcementfederateddeepwirelessnetworkssurvey
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 is a survey, and its pith is a claim about how to read the field: research on AI-enabled UAV wireless networks is best organized by AI subfield rather than by application type. It argues that supervised and unsupervised machine learning, reinforcement learning, and federated learning each have distinct roles in drone networks, and that federated learning in particular is the practical route to onboard intelligence for battery- and compute-limited drones. It also claims to cover two areas the closest prior survey did not: federated learning for UAVs and reinforcement learning for IRS-equipped UAVs supporting millimeter-wave bands. A sympathetic reader would care because the survey provides both a structured entry point for newcomers and a concrete list of open problems, from client selection and communication overhead in federated learning to the literature's heavy tilt toward path planning.

What carries the argument

The load-bearing structure is the survey's taxonomy: three AI subfields—supervised/unsupervised learning, reinforcement learning, and federated learning—each introduced with its standard algorithms and then applied to UAV problems. The pedagogical mechanisms that make the taxonomy concrete are the worked Q-learning grid example, with the Bellman update $Q_{\mathrm{new}}(s_t,a_t) = (1-\alpha)Q_{\mathrm{old}}(s_t,a_t) + \alpha(R_{t+1} + \gamma \max_a Q(s_{t+1},a))$, and the federated learning update $w_{t+1}^k = w_t - \eta \nabla \ell(w_t, B)$, which shows how clients send model updates rather than raw data. These mechanisms do the work of showing readers what each AI area is before the survey reviews how it has been applied.

What would settle it

Take a random sample of about 30 papers classified in the survey's tables, read each paper's method section, and check whether the listed technique (for example, DQN, Q-learning, or federated learning) is actually used; a mismatch rate clearly above a few percent would show the survey misrepresents the literature it claims to map. A systematic search of the same period that surfaces a major AI-for-UAV work the survey omits would similarly weaken the holistic claim.

Watch

Extended reading notes

Core claim

The paper's central claim is that the state of the art in AI-enabled UAV networks can be holistically surveyed by walking through three AI subfields and mapping each onto UAV applications. For supervised and unsupervised learning, the applications are predictive deployment, channel estimation, UAV detection, and imaging; for reinforcement learning, they are path planning, scheduling, resource management, and IRS-enabled networks; for federated learning, they are resource allocation, path control, FANET security, content caching, and sensing. The survey pairs each subfield with a short tutorial (SVM, K-means, GMM, CNN, RNN; Q-learning, DQN, DDPG; SGD-based federated averaging) and with discussion sections identifying limitations and future work. It further claims that practical adoption is constrained by onboard computing limits and by regulations that lag autonomous-UAV research, and it flags a suspicion that published ML results may overstate accuracy because of dataset bias.

Load-bearing premise

The survey's map of the field is only as reliable as its 177 cited references: no systematic search or inclusion protocol is given and the authors summarize results second-hand, so a misread or omitted key work would weaken the claim to be holistic and comprehensive.

Editorial extensions

If this is right

  • A newcomer can enter the field through the per-subfield primers and the worked Q-learning example without prior AI background.
  • Federated learning is presented as the practical way to run machine learning on constrained drones, since it shares model updates rather than raw data and keeps learning active even when a drone is offline.
  • The survey's tables classify existing RL path-planning works by technique, dimension, single- or multi-agent setup, and constraints, which makes gaps visible, such as the scarcity of resource-allocation and event-scheduling studies.
  • The open problems named in the survey—client selection, convergence guarantees, communication overhead, stragglers, and regulation—define a concrete agenda for future work.
  • The survey's warning that reported ML results consistently beat empirical baselines implies that readers should treat performance gains in this literature with caution.

Reading between the lines

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

  • The survey leaves implicit that a quantitative meta-analysis could test its suspicion of 'fake ML accuracy' by correlating reported gains with dataset size, test-set bias, and whether a classical baseline was tuned.
  • Its framing of federated learning suggests a testable extension: measuring whether the communication overhead of model updates outweighs the energy saved by not sending raw data, for realistic drone fleets with limited bandwidth.
  • The regulation discussion gestures at a policy consequence: harmonized rules for autonomous drones could unlock realistic field trials, which are currently missing from almost every cited work.
  • The IRS-UAV material points beyond the surveyed RL examples toward a broader design space where drone position and reflecting-surface phase shifts are optimized jointly; that space is only sketched in the paper.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Request a human review

A listed scientist reviews the paper for a fee and the review publishes here regardless of verdict. See the reviewers or get listed.

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. This manuscript surveys the application of artificial intelligence to UAV-enabled wireless networks, organized by AI subfield: supervised/unsupervised learning, reinforcement learning, and federated learning. It covers 177 references, provides tutorial introductions to each AI area, discusses applications such as deployment, channel estimation, detection, navigation, resource management, IRS-enabled UAV networks, and FL-based security and caching, and closes each section with limitations and future directions. The paper claims to offer a holistic overview that goes beyond the closest prior survey [23] by covering federated learning and IRS-enabled UAV deployments.

Significance. If its coverage is accurate, the survey would be a useful entry point for researchers new to AI-enabled UAV networks, particularly because of its FL section and its treatment of IRS-equipped UAVs, which are not covered in the closest prior survey. The explicit organization by AI subfield and the inclusion of tutorial material are genuine strengths. The paper is not a derivation-based work, so the assessment rests on the accuracy and representativeness of the survey content rather than on mathematical soundness. The authors also include candid critical discussion of common pitfalls in ML-for-wireless papers, which adds value.

major comments (3)
  1. [Section III-A] The tutorial on reinforcement learning contains two load-bearing errors for a survey that advertises itself as accessible to readers with no AI background. First, the discounted return is written as G_t = ∑_t γ^{t-1} R_t, which is not a well-formed definition; the standard definition is G_t = ∑_{k=0}^{∞} γ^k R_{t+k+1}. Second, the model-based/model-free distinction is misstated: the paper says model-based RL updates the value function based on the model rather than experience, and that in model-free RL 'the agent cannot predict the future.' In standard RL terminology, model-based RL uses an explicit model of transition dynamics and rewards for planning (which may include simulated experience), while model-free RL learns values or policies directly from real experience without learning such a model. These errors should be corrected because the survey's stated audience includes readers with no prior AI knowledge.
  2. [Table II and References [118], [119], [136]] Several citation-handling errors directly undermine the auditability of the 'holistic overview' claim. Reference [118] and reference [136] list the same title (Liu, Liu, and Chen, 'Machine learning empowered trajectory and passive beamforming design in UAV-RIS wireless networks'), one as arXiv:2010.02749 and the other as an IEEE JSAC paper, and Table II's [118] entry for Decaying DQN is inconsistent with the discussion of [136] in Section III-C2. In addition, reference [119] is assigned the same arXiv identifier (2010.02749) as [118], which cannot be correct for a distinct multi-agent NOMA paper. These are not cosmetic problems: a reader cannot independently verify which work is being summarized, and the survey's central value proposition is that it provides a reliable map of the literature.
  3. [Section I-B and References] The paper claims in Section I-B to provide 'a holistic overview of the state of the art research' and 'comprehensive' coverage, but it does not state any search, inclusion, or exclusion protocol for the 177 references. As a result, the representativeness of the survey is asserted rather than demonstrated. This is a fixable issue: the authors should either add a short methodology paragraph describing the databases, search terms, and inclusion criteria, or soften the 'holistic/comprehensive' claims to 'broad overview' to match the actual scope.
minor comments (5)
  1. [Section I-A] The text says 'a motion planning for UAVs guide was presented in [19] and a survey for UAV traffic monitoring is provided in [20],' but in the reference list [19] is the Chen et al. ANN tutorial and [20] is the Goerzen et al. motion planning survey; the citations appear to be swapped.
  2. [Table I] Reference [54] is listed as a pedestrian detection paper with 'Faster R-CNN+Region proposal network(RPN),' but the reference list entry [54] is Zhang, Tang, and Roemer, 'Probabilistic weather forecasting analysis for unmanned aerial vehicle path planning,' which is also a near-duplicate of [5]. The table entry should be matched to the correct source or removed.
  3. [References [52] and [60]] References [52] and [60] are the same paper (Bazi and Melgani, 'Convolutional SVM networks for object detection in UAV imagery,' IEEE TGRS 2018). One duplicate should be removed and the in-text citations renumbered.
  4. [References [21] and [64]] Reference [64] duplicates reference [21] (Carrio et al., 'A review of deep learning methods and applications for unmanned aerial vehicles'); the duplicate should be removed.
  5. [Throughout] There are numerous typographical and consistency issues, including 'free-model RL' instead of 'model-free RL' in Section III-B, 'concurrent neural networks' instead of 'convolutional neural networks' for [39], 'te implement' in Section III-C1, and inconsistent spacing in 'UA V' throughout the text. A careful copyedit is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: survey makes no derivations or fitted predictions; self-citations are background only.

full rationale

This is a survey paper whose central claim (Sec. I-B) is to provide a holistic, organized overview of AI-enabled UAV networks with added coverage of federated learning and IRS-enabled UAV deployments relative to prior survey [23]. The paper fits no parameters, derives no equations from which a 'prediction' is later extracted, and does not define any input quantity in terms of its output. The only author self-citations, [12]-[16], are used in Sec. I to support background statements about UAV battery and computational limitations; they are not invoked to justify the survey's taxonomy, coverage choices, or any technical conclusion. The claimed value-added over [23] is an external, auditable comparison: the absence or presence of FL and IRS-UAV topics can be checked against the cited literature. Consequently, none of the enumerated circularity patterns applies. Technical issues noted in the text, such as the RL return formula and the model-based/model-free distinction in Sec. III-A, or reference-list inconsistencies around [118], [119], and [136], are accuracy and verifiability concerns, not circularity, and do not raise the circularity score.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

No new mathematical model or data analysis, so there are no free parameters and no invented entities. The ledger captures the two background trusts the survey is built on: fidelity of secondary summaries and correctness of its tutorial formalism.

assumptions (2)
  • domain assumption The cited papers are accurately represented and their reported numerical results are correct as summarized.
    The survey's central value is a map of prior literature; Sections II.C, III.C, and IV.C rely on second-hand summaries without independent verification.
  • standard math Textbook definitions of ML, RL, and FL are standard and correctly applied.
    Sections II.A, III.A, and IV.A give tutorial definitions; Section III.A's discounted-return equation deviates from the standard definition, so this axiom is only partially satisfied.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Artificial Intelligence for UAV-enabled Wireless Networks: A Survey." pith.science (2026). https://pith.science/paper/TU2WTJQI

@misc{pith2026200911522,
  author       = {Pith},
  title        = {Pith review of: Artificial Intelligence for UAV-enabled Wireless Networks: A Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TU2WTJQI}},
  note         = {Machine review of arXiv:2009.11522}
}
read the original abstract

Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. We also highlight the limits of the existing works and outline some potential future applications of AI for UAV networks.

Figures

Figures reproduced from arXiv: 2009.11522 by the authors.

Figure 1
Figure 1. Survey organization. Supervised learning Tasks Regression Classification E.g. SVM/GMM/AN N/CNN… Labeled data Unlabeled data Unsupervised learning Tasks Clustering Data generation E.g. GAN/AE/K￾means Reinforcement learning Machine learning Tasks Path planning Resource management E.g. Q-learning Deep Q-net [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Machine learning overview. need to provide the algorithm with a set of training data that contains each UAV characteristics and its associated label (the price). The dataset is usually divided into a training set and a test set. The training set is used to learn the relationship between the input and the output and the test set is used to validate the model by measuring its accuracy. The supervised problems are ofte… view at source ↗
Figure 3
Figure 3. Neural network architectures. 5) Convolutional neural networks (CNNs) CNN is another type of ANN designed initially for computer vision tasks. A CNN usually takes an image as an input, assigns learnable weights and biases that are updated according to a specific algorithm. The CNN architecture is characterized by the convolutional layers which extract high-level features from the image that will be used later. Techn… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Reinforcement learning elements. for each type of input and then a final NN is used to classify the output. To conclude, ML supervised and unsupervised frameworks have often successfully overcome many challenges by provid￾ing intelligent solutions for various problems …
Figure 5
Figure 5. Figure 5: Grid map. Gradient (DPG) algorithm was first proposed in Deepmind’s publication in 2014 [67] based on an Actor-Critic off policy approach. We refer readers that are not familiar with Actor￾Critic RL methods to [17, Chapter 13]. For the sake of simplicity, let’s keep in…
Figure 6
Figure 6. Figure 6: Exploration/Exploitation dilemma. UAV from the network separately. It is highly recommended nowadays to equip UAVs with the ability to make intelligent decisions by implementing a high level of control. Achieving such a high autonomy for UAVs is a challenging task due …
Figure 7
Figure 7. Figure 7: UAV-IR use case scenarios. parameters is marked if the UAV’s limited energy is considered as a constraint to the problem, so any work where the UAV’s energy consumption is minimized without imposing a limit on the battery level will not be marked. In the coming section…
Figure 8
Figure 8. Figure 8: RL taxonomy. limited samples and more. On the bright side, several large companies and research labs have been working on producing alternatives to RL such as Evolution Strategies (ES) proposed by OpenAI [149] . To sum up, even if RL is not the ultimate solution for al…
Figure 9
Figure 9. Figure 9: Federated learning principle. A. FL Principle Without loss of generality, we provide a comprehensive explanation for FL algorithm for a setup of a network of UAVs that are served by a terrestrial base station. As a typical task, we suppose that the UAVs are processing …

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

174 extracted references · 76 canonical work pages

  1. [23]

    A survey on machine-learning techniques for UA V-based communications,

    P. S. Bithas, E. T. Michailidis, N. Nomikos, D. V ouyioukas, and A. G. Kanatas, “A survey on machine-learning techniques for UA V-based communications,” Sensors, vol. 19, no. 23, p. 5170, 2019

    2019

  2. [136]

    Machine learning empowered trajectory and passive beamforming design in uav-ris wireless networks,

    X. Liu, Y . Liu, and Y . Chen, “Machine learning empowered trajectory and passive beamforming design in uav-ris wireless networks,” IEEE Journal on Selected Areas in Communications , pp. 1–1, 2020

    work page 2020

  3. [119]

    Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networks

    R. Zhong, X. Liu, Y . Liu, and Y . Chen, “Multi-agent reinforcement learning in NOMA-aided UA V networks for cellular offloading,”Avail- able online: arxiv.org/abs/2010.02749

    work page Pith review arXiv 2010

  4. [1]

    UA V communications for 5G and beyond: Recent advances and future trends,

    B. Li, Z. Fei, and Y . Zhang, “UA V communications for 5G and beyond: Recent advances and future trends,” IEEE Internet of Things Journal , vol. 6, no. 2, pp. 2241–2263, 2019

    2019

  5. [2]

    Accessing from the sky: A tutorial on UA V communications for 5G and beyond,

    Y . Zeng, Q. Wu, and R. Zhang, “Accessing from the sky: A tutorial on UA V communications for 5G and beyond,” Proceedings of the IEEE , vol. 107, no. 12, pp. 2327–2375, 2019

    2019

  6. [3]

    Airborne communication networks: A survey,

    X. Cao, P. Yang, M. Alzenad, X. Xi, D. Wu, and H. Yanikomeroglu, “Airborne communication networks: A survey,” IEEE Journal on Selected Areas in Communications , vol. 36, no. 9, pp. 1907–1926, 2018

    1907

  7. [4]

    A tutorial on UA Vs for wireless networks: Applications, challenges, and open problems,

    M. Mozaffari, W. Saad, M. Bennis, Y .-H. Nam, and M. Debbah, “A tutorial on UA Vs for wireless networks: Applications, challenges, and open problems,” IEEE Communications Surveys & Tutorials , vol. 21, no. 3, pp. 2334–2360, 2019

    2019

  8. [5]

    Probabilistic weather forecast- ing analysis for unmanned aerial vehicle path planning,

    B. Zhang, L. Tang, and M. Roemer, “Probabilistic weather forecast- ing analysis for unmanned aerial vehicle path planning,” Journal of Guidance, Control, and Dynamics , vol. 37, no. 1, pp. 309–312, 2014

    2014

Show all 174 references
  1. [6]

    A survey of unmanned aerial vehicles (UA V) for traffic surveillance,

    A. Puri, “A survey of unmanned aerial vehicles (UA V) for traffic surveillance,” Department of computer science and engineering, Uni- versity of South Florida , pp. 1–29, 2005

    2005

  2. [7]

    Plot-scale rice grain yield estimation using UA V-based remotely sensed images via CNN with time-invariant deep features decomposition,

    Q. Yang, L. Shi, and L. Lin, “Plot-scale rice grain yield estimation using UA V-based remotely sensed images via CNN with time-invariant deep features decomposition,” in IGARSS IEEE International Geo- science and Remote Sensing Symposium , 2019, pp. 7180–7183

    2019

  3. [8]

    A novel deep learning method to identify single tree species in UA V-based hyperspectral images,

    G. T. Miyoshi, M. d. S. Arruda, L. P. Osco, J. Marcato Junior, D. N. Gonc ¸alves, N. N. Imai, A. M. G. Tommaselli, E. Honkavaara, and W. N. Gonc ¸alves, “A novel deep learning method to identify single tree species in UA V-based hyperspectral images,”Remote Sensing, vol. 12, n...

    2020

  4. [9]

    Weed location and recognition based on UA V imaging and deep learning,

    R. Zhang, C. Wang, X. Hu, Y . Liu, S. Chen et al., “Weed location and recognition based on UA V imaging and deep learning,” International Journal of Precision Agricultural Aviation , vol. 3, no. 1, 2020

    2020

  5. [10]

    Machine learning for predictive on-demand deployment of UA Vs for wireless communications,

    Q. Zhang, M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Machine learning for predictive on-demand deployment of UA Vs for wireless communications,” in Proc. IEEE Global Communications Conference (GLOBECOM), 2018

    2018

  6. [11]

    Wireless communications with unmanned aerial vehicles: Opportunities and challenges,

    Y . Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: Opportunities and challenges,” IEEE Com- munications Magazine, vol. 54, no. 5, pp. 36–42, May 2016

    2016

  7. [12]

    Stochastic geometry- based analysis of airborne base stations with laser-powered UA Vs,

    M. Lahmeri, M. A. Kishk, and M.-S. Alouini, “Stochastic geometry- based analysis of airborne base stations with laser-powered UA Vs,” IEEE Communications Letters , vol. 24, no. 1, pp. 173–177, 2020

    2020

  8. [13]

    Optimal deployment of tethered drones for maximum cellular coverage in user clusters,

    O. M. Bushnaq, M. A. Kishk, A. C ¸ elik, M. S. Alouini, and T. Y . Al-Naffouri, “Optimal deployment of tethered drones for maximum cellular coverage in user clusters,” IEEE Transactions on Wireless Communications, to appear

  9. [14]

    Performance evaluation of UA V-enabled cellular networks with battery-limited drones,

    Y . Qin, M. A. Kishk, and M. S. Alouini, “Performance evaluation of UA V-enabled cellular networks with battery-limited drones,” IEEE Communications Letters, vol. 24, no. 12, pp. 2664–2668, 2020

    2020

  10. [15]

    On the 3-D placement of airborne base stations using tethered UA Vs,

    M. A. Kishk, A. Bader, and M.-S. Alouini, “On the 3-D placement of airborne base stations using tethered UA Vs,” IEEE Transactions on Communications, vol. 68, no. 8, pp. 5202–5215, 2020

    2020

  11. [16]

    Aerial base station deployment in 6G cellular networks using tethered drones: The mobility and endurance tradeoff,

    M. Kishk, A. Bader, and M. S. Alouini, “Aerial base station deployment in 6G cellular networks using tethered drones: The mobility and endurance tradeoff,” IEEE Vehicular Technology Magazine , vol. 15, no. 4, pp. 103–111, 2020

    2020

  12. [17]

    R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. MIT press, 2018

    2018

  13. [18]

    Multi-agent reinforcement learning-based resource allocation for UA V networks,

    J. Cui, Y . Liu, and A. Nallanathan, “Multi-agent reinforcement learning-based resource allocation for UA V networks,” IEEE Transac- tions on Wireless Communications , vol. 19, no. 2, pp. 729–743, 2019

    2019

  14. [19]

    Artificial neural networks-based machine learning for wireless networks: A tutorial,

    M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial neural networks-based machine learning for wireless networks: A tutorial,” IEEE Communications Surveys Tutorials , vol. 21, no. 4, pp. 3039–3071, 2019

    2019

  15. [20]

    A survey of motion planning al- gorithms from the perspective of autonomous UA V guidance,

    C. Goerzen, Z. Kong, and B. Mettler, “A survey of motion planning al- gorithms from the perspective of autonomous UA V guidance,”Journal of Intelligent and Robotic Systems , vol. 57, no. 1-4, p. 65, 2010

    2010

  16. [21]

    A review of deep learning methods and applications for unmanned aerial vehicles,

    A. Carrio, C. Sampedro, A. Rodriguez-Ramos, and P. Campoy, “A review of deep learning methods and applications for unmanned aerial vehicles,” Journal of Sensors , 2017

    2017

  17. [22]

    An overview of machine learning applied in wireless UA V networks,

    P. V . Klaine, R. D. Souza, L. Zhang, and M. Imran, “An overview of machine learning applied in wireless UA V networks,” Wiley 5G Ref: The Essential 5G Reference Online , pp. 1–15, 2019

    2019

  18. [24]

    Learning radio maps for UA V- aided wireless networks: A segmented regression approach,

    J. Chen, U. Yatnalli, and D. Gesbert, “Learning radio maps for UA V- aided wireless networks: A segmented regression approach,” in Proc. IEEE International Conference on Communications (ICC) , 2017, pp. 1–6

    2017

  19. [25]

    Predictive deployment of uav base stations in wireless networks: Ma- chine learning meets contract theory,

    Q. Zhang, W. Saad, M. Bennis, X. Lu, M. Debbah, and W. Zuo, “Predictive deployment of uav base stations in wireless networks: Ma- chine learning meets contract theory,” IEEE Transactions on Wireless Communications, pp. 1–1, 2020

    2020

  20. [26]

    Trajectory prediction of UA V in smart city using recurrent neural networks,

    K. Xiao, J. Zhao, Y . He, and S. Yu, “Trajectory prediction of UA V in smart city using recurrent neural networks,” in Proc. of IEEE International Conference on Communications (ICC) , May 2019, pp. 1–6

    2019

  21. [27]

    A unified framework for joint mobility prediction and object profiling of drones in UA V networks,

    H. Peng, A. Razi, F. Afghah, and J. Ashdown, “A unified framework for joint mobility prediction and object profiling of drones in UA V networks,” Journal of Communications and Networks , vol. 20, no. 5, pp. 434–442, 2018

    2018

  22. [28]

    Relay positioning strategy for traffic data collection of multiple unmanned aerial vehi- cles using hybrid optimization systems: A FANET-based case study,

    J. Jailton, T. Carvalho, J. Ara ´ujo, and R. Franc ˆes, “Relay positioning strategy for traffic data collection of multiple unmanned aerial vehi- cles using hybrid optimization systems: A FANET-based case study,” Wireless Communications and Mobile Computing , 2017

    2017

  23. [29]

    Air-to-air path loss prediction based on machine learning methods in urban environments,

    Y . Zhang, J. Wen, G. Yang, Z. He, and X. Luo, “Air-to-air path loss prediction based on machine learning methods in urban environments,” Wireless Communications and Mobile Computing , vol. 2018

    2018

  24. [30]

    Generative neural network channel modeling for millimeter-wave uav communication,

    W. Xia, S. Rangan, M. Mezzavillla, A. Lozano, G. Geraci, V . Semkin, and G. Loianno, “Generative neural network channel modeling for millimeter-wave uav communication,” Available online: arxiv.org/abs/2012.09133

    2012 arXiv

  25. [31]

    Predictive estimation of the optimal signal strength from unmanned aerial vehicle over internet of things using ANN,

    S. Alsamhi, O. Ma, and S. Ansari, “Predictive estimation of the optimal signal strength from unmanned aerial vehicle over internet of things using ANN,” Available online: arxiv.org/abs/1805.07614. 20

    arXiv

  26. [32]

    Machine-learning and 3D point-cloud based signal power path loss model for the deployment of wireless commu- nication systems,

    Y . Egi and C. E. Otero, “Machine-learning and 3D point-cloud based signal power path loss model for the deployment of wireless commu- nication systems,” IEEE Access, vol. 7, pp. 42 507–42 517, 2019

    2019

  27. [33]

    Artificial neural network optimal modeling and optimization of UA V measurements for mobile com- munications using the L-SHADE algorithm,

    S. K. Goudos, G. V . Tsoulos, G. Athanasiadou, M. C. Batistatos, D. Zarbouti, and K. E. Psannis, “Artificial neural network optimal modeling and optimization of UA V measurements for mobile com- munications using the L-SHADE algorithm,” IEEE Transactions on Antennas and Propaga...

    2019

  28. [34]

    A hybrid approach of learning and model-based channel prediction for communication relay UA Vs in dynamic urban environments,

    P. Ladosz, H. Oh, G. Zheng, and W.-H. Chen, “A hybrid approach of learning and model-based channel prediction for communication relay UA Vs in dynamic urban environments,”IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2370–2377, 2019

    2019

  29. [35]

    Application of an ensemble method to UA V power modeling for cellular communications,

    S. K. Goudos and G. Athanasiadou, “Application of an ensemble method to UA V power modeling for cellular communications,” IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 11, pp. 2340– 2344, 2019

    2019

  30. [36]

    Machine learning based rapid 3D channel modeling for UA V communication networks,

    J.-L. Wang, Y .-R. Li, A. B. Adege, L.-C. Wang, S.-S. Jeng, and J.- Y . Chen, “Machine learning based rapid 3D channel modeling for UA V communication networks,” inProc. 16th IEEE Annual Consumer Communications & Networking Conference (CCNC) , 2019, pp. 1–5

    2019

  31. [37]

    A proposal for path loss prediction in urban environments using support vector regression,

    R. D. Timoteo, D. C. Cunha, and G. D. Cavalcanti, “A proposal for path loss prediction in urban environments using support vector regression,” in Proc. Advanced Int. Conf. Telecommun , 2014, pp. 1–5

    2014

  32. [38]

    Real- time UA V sound detection and analysis system,

    J. Kim, C. Park, J. Ahn, Y . Ko, J. Park, and J. C. Gallagher, “Real- time UA V sound detection and analysis system,” in IEEE Sensors Applications Symposium (SAS) , 2017, pp. 1–5

    2017

  33. [39]

    Development of an acoustic system for UA V detection,

    C. Dumitrescu, M. Minea, I. M. Costea, I. Cosmin Chiva, and A. Seme- nescu, “Development of an acoustic system for UA V detection,” Sensors, vol. 20, no. 17, p. 4870, 2020

    2020

  34. [40]

    UA V detection system with multiple acoustic nodes using machine learning models,

    B. Yang, E. T. Matson, A. H. Smith, J. E. Dietz, and J. C. Gallagher, “UA V detection system with multiple acoustic nodes using machine learning models,” in Proc. IEEE International Conference on Robotic Computing (IRC), 2019, pp. 493–498

    2019

  35. [41]

    Empirical study of drone sound detection in real-life environment with deep neural networks,

    S. Jeon, J.-W. Shin, Y .-J. Lee, W.-H. Kim, Y . Kwon, and H.-Y . Yang, “Empirical study of drone sound detection in real-life environment with deep neural networks,” in Proc. IEEE European Signal Processing Conference (EUSIPCO), 2017, pp. 1858–1862

    2017

  36. [42]

    Convolutional neural networks for analyzing unmanned aerial vehicles sound,

    S. Li, H. Kim, S. Lee, J. C. Gallagher, D. Kim, S. Park, and E. T. Matson, “Convolutional neural networks for analyzing unmanned aerial vehicles sound,” pp. 862–866, 2018

    2018

  37. [43]

    Machine learning-based delay-aware UA V detection over encrypted Wi-Fi traffic,

    A. Alipour-Fanid, M. Dabaghchian, N. Wang, P. Wang, L. Zhao, and K. Zeng, “Machine learning-based delay-aware UA V detection over encrypted Wi-Fi traffic,” inProc. IEEE Conference on Communications and Network Security (CNS) , 2019, pp. 1–7

    2019

  38. [44]

    Machine-learning identification of airborne UA V-UEs based on LTE radio measurements,

    R. Amorim, J. Wigard, H. Nguyen, I. Z. Kovacs, and P. Mogensen, “Machine-learning identification of airborne UA V-UEs based on LTE radio measurements,” in Proc. IEEE Globecom Workshops (GC Wk- shps), 2017, pp. 1–6

    2017

  39. [45]

    A UA V detection algorithm based on an artificial neural network,

    H. Zhang, C. Cao, L. Xu, and T. A. Gulliver, “A UA V detection algorithm based on an artificial neural network,” IEEE Access, vol. 6, pp. 24 720–24 728, 2018

    2018

  40. [46]

    Micro-UA V detection and classification from RF fingerprints using machine learning techniques,

    M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, “Micro-UA V detection and classification from RF fingerprints using machine learning techniques,” in Proc. IEEE Aerospace Conference , 2019, pp. 1–13

    2019

  41. [47]

    RF-based drone detection and identification using deep learning approaches: An initiative towards a large open source drone database,

    M. F. Al-Sad, A. Al-Ali, A. Mohamed, T. Khattab, and A. Erbad, “RF-based drone detection and identification using deep learning approaches: An initiative towards a large open source drone database,” Future Generation Computer Systems , vol. 100, pp. 86–97, 2019

    2019

  42. [48]

    A comparison of convolutional object detectors for real-time drone tracking using a PTZ camera,

    J. Park, D. H. Kim, Y . S. Shin, and S.-h. Lee, “A comparison of convolutional object detectors for real-time drone tracking using a PTZ camera,” inProc. International Conference on Control, Automation and Systems (ICCAS). IEEE, 2017, pp. 696–699

    2017

  43. [49]

    Review of modern UA V detection algorithms using methods of computer vision,

    L. I. Ivanov, N. A. Obukhova, and P. S. Baranov, “Review of modern UA V detection algorithms using methods of computer vision,” inProc. IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) , 2020, pp. 322–325

    2020

  44. [50]

    Car detection from low- altitude UA V imagery with the faster R-CNN,

    Y . Xu, G. Yu, Y . Wang, X. Wu, and Y . Ma, “Car detection from low- altitude UA V imagery with the faster R-CNN,” Journal of Advanced Transportation, 2017

    2017

  45. [51]

    Nazr-CNN: Fine-grained classification of UA V imagery for damage assessment,

    N. Attari, F. Ofli, M. Awad, J. Lucas, and S. Chawla, “Nazr-CNN: Fine-grained classification of UA V imagery for damage assessment,” in Proc. IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2017, pp. 50–59

    2017

  46. [53]

    Defect detection from UA V images based on region-based CNNs,

    M. Lan, Y . Zhang, L. Zhang, and B. Du, “Defect detection from UA V images based on region-based CNNs,” in Proc. IEEE International Conference on Data Mining Workshops (ICDMW), 2018, pp. 385–390

    2018

  47. [54]

    Probabilistic weather forecast- ing analysis for unmanned aerial vehicle path planning,

    B. Zhang, L. Tang, and M. Roemer, “Probabilistic weather forecast- ing analysis for unmanned aerial vehicle path planning,” Journal of Guidance, Control, and Dynamics , vol. 37, no. 1, pp. 309–312, 2013

    2013

  48. [55]

    A deep CNN-based framework for enhanced aerial imagery registration with applications to UA V geolocalization,

    A. Nassar, K. Amer, R. ElHakim, and M. ElHelw, “A deep CNN-based framework for enhanced aerial imagery registration with applications to UA V geolocalization,” inProc. IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 1513–1523

    2018

  49. [56]

    DroNet: Efficient convolutional neural network detector for real-time UA V applications,

    C. Kyrkou, G. Plastiras, T. Theocharides, S. I. Venieris, and C.-S. Bouganis, “DroNet: Efficient convolutional neural network detector for real-time UA V applications,” in Proc. Design, Automation & Test in Europe Conference & Exhibition (DATE) . IEEE, 2018, pp. 967–972

    2018

  50. [57]

    Towards automatic inspection: crack recognition based on quadrotor UA V-taken images,

    J. Vazquez-Nicolas, E. Zamora, I. Gonzalez-Hernandez, R. Lozano, and H. Sossa, “Towards automatic inspection: crack recognition based on quadrotor UA V-taken images,” inProc. IEEE International Conference on Unmanned Aircraft Systems (ICUAS) , 2018, pp. 654–659

    2018

  51. [58]

    Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UA Vs,

    A. A. d. Santos, J. Marcato Junior, M. S. Ara ´ujo, D. R. Di Martini, E. C. Tetila, H. L. Siqueira, C. Aoki, A. Eltner, E. T. Matsubara, H. Pistori et al. , “Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UA Vs,” Sen...

    2019

  52. [59]

    Land cover classification from fused DSM and UA V images using convolutional neural networks,

    H. A. Al-Najjar, B. Kalantar, B. Pradhan, V . Saeidi, A. A. Halin, N. Ueda, and S. Mansor, “Land cover classification from fused DSM and UA V images using convolutional neural networks,” Remote Sens- ing, vol. 11, no. 12, p. 1461, 2019

    2019

  53. [60]

    Convolutional SVM networks for object detection in UA V imagery,

    Y . Bazi and F. Melgani, “Convolutional SVM networks for object detection in UA V imagery,” IEEE transactions on geoscience and remote sensing, vol. 56, no. 6, pp. 3107–3118, 2018

    2018

  54. [61]

    Visual detection of green mangoes by an unmanned aerial vehicle in orchards based on a deep learning method,

    J. Xiong, Z. Liu, S. Chen, B. Liu, Z. Zheng, Z. Zhong, Z. Yang, and H. Peng, “Visual detection of green mangoes by an unmanned aerial vehicle in orchards based on a deep learning method,”Biosystems Engineering, vol. 194, pp. 261–272, 2020

    2020

  55. [62]

    Detection of maize tassels from UA V RGB imagery with faster R-CNN,

    Y . Liu, C. Cen, Y . Che, R. Ke, Y . Ma, and Y . Ma, “Detection of maize tassels from UA V RGB imagery with faster R-CNN,” Remote Sensing, vol. 12, no. 2, p. 338, 2020

    2020

  56. [63]

    A convolutional neural network approach for counting and geolocating citrus-trees in UA V multispectral imagery,

    L. P. Osco, M. d. S. de Arruda, J. M. Junior, N. B. da Silva, A. P. M. Ramos, ´E. A. S. Moryia, N. N. Imai, D. R. Pereira, J. E. Creste, E. T. Matsubara et al., “A convolutional neural network approach for counting and geolocating citrus-trees in UA V multispectral imagery,” I...

    2020

  57. [64]

    A review of deep learning methods and applications for unmanned aerial vehicles,

    A. Carrio, C. Sampedro, A. Rodriguez-Ramos, and P. Campoy, “A review of deep learning methods and applications for unmanned aerial vehicles,” Journal of Sensors , vol. 2017, 2017

    2017

  58. [65]

    On-device machine learning: An algorithms and learning theory perspective,

    S. Dhar, J. Guo, J. Liu, S. Tripathi, U. Kurup, and M. Shah, “On-device machine learning: An algorithms and learning theory perspective,” Available online: arxiv.org/abs/1911.00623

    1911 arXiv

  59. [66]

    Human-level control through deep reinforcement learning,

    V . Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” nature, vol. 518, no. 7540, pp. 529–533, 2015

    2015

  60. [67]

    Deterministic policy gradient algorithms,

    D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Ried- miller, “Deterministic policy gradient algorithms,” 2014

    2014

  61. [68]

    Neural fitted q iteration–first experiences with a data efficient neural reinforcement learning method,

    M. Riedmiller, “Neural fitted q iteration–first experiences with a data efficient neural reinforcement learning method,” in Proc. European Conference on Machine Learning . Springer, 2005, pp. 317–328

    2005

  62. [69]

    Autonomous navigation of UA V by using real-time model-based reinforcement learning,

    N. Imanberdiyev, C. Fu, E. Kayacan, and I.-M. Chen, “Autonomous navigation of UA V by using real-time model-based reinforcement learning,” in Proc. IEEE 14th International Conference on Control, Automation, Robotics and Vision (ICARCV) , 2016, pp. 1–6

    2016

  63. [70]

    Reinforcement learning in multiple-UA V networks: Deployment and movement design,

    X. Liu, Y . Liu, and Y . Chen, “Reinforcement learning in multiple-UA V networks: Deployment and movement design,” IEEE Transactions on Vehicular Technology, vol. 68, no. 8, pp. 8036–8049, 2019

    2019

  64. [71]

    Cooperative and distributed reinforcement learning of drones for field coverage,

    H. X. Pham, H. M. La, D. Feil-Seifer, and A. Nefian, “Cooperative and distributed reinforcement learning of drones for field coverage,” Available online: arxiv.org/abs/1803.07250

    arXiv

  65. [72]

    Trajectory design and power control for multi-UA V assisted wireless networks: A machine learning approach,

    X. Liu, Y . Liu, Y . Chen, and L. Hanzo, “Trajectory design and power control for multi-UA V assisted wireless networks: A machine learning approach,” IEEE Transactions on Vehicular Technology, vol. 68, no. 8, pp. 7957–7969, 2019

    2019

  66. [73]

    Optimized trajectory design in UA V based cellular networks for 3D users: A double Q-learning approach,

    X. Liu, M. Chen, and C. Yin, “Optimized trajectory design in UA V based cellular networks for 3D users: A double Q-learning approach,” 2019

    2019

  67. [74]

    Deep reinforcement learning for UA V navigation through massive MIMO 21 technique,

    H. Huang, Y . Yang, H. Wang, Z. Ding, H. Sari, and F. Adachi, “Deep reinforcement learning for UA V navigation through massive MIMO 21 technique,” IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 1117–1121, 2020

    2020

  68. [75]

    Energy-efficient UA V control for effective and fair communication coverage: A deep reinforcement learning approach,

    C. H. Liu, Z. Chen, J. Tang, J. Xu, and C. Piao, “Energy-efficient UA V control for effective and fair communication coverage: A deep reinforcement learning approach,” IEEE Journal on Selected Areas in Communications, vol. 36, no. 9, pp. 2059–2070, 2018

    2018

  69. [76]

    Deep reinforcement learning for drone delivery,

    G. Mu ˜noz, C. Barrado, E. C ¸ etin, and E. Salami, “Deep reinforcement learning for drone delivery,” Drones, vol. 3, no. 3, p. 72, 2019

    2019

  70. [77]

    Self-training by reinforce- ment learning for full-autonomous drones of the future*,

    K. Kersandt, G. Munoz, and C. Barrado, “Self-training by reinforce- ment learning for full-autonomous drones of the future*,” in Proc. IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) , 2018, pp. 1–10

    2018

  71. [78]

    Waitress quadcopter explores how to serve drinks by reinforcement learning,

    E. Camci and E. Kayacan, “Waitress quadcopter explores how to serve drinks by reinforcement learning,” in Proc. IEEE region 10 conference (TENCON), 2016, pp. 28–32

    2016

  72. [79]

    Autonomous navigation of UA Vs in large-scale complex environments: A deep reinforce- ment learning approach,

    C. Wang, J. Wang, Y . Shen, and X. Zhang, “Autonomous navigation of UA Vs in large-scale complex environments: A deep reinforce- ment learning approach,” IEEE Transactions on Vehicular Technology, vol. 68, no. 3, pp. 2124–2136, 2019

    2019

  73. [80]

    Autonomous UA V navigation: A DDPG-based deep reinforcement learning ap- proach,

    O. Bouhamed, H. Ghazzai, H. Besbes, and Y . Massoud, “Autonomous UA V navigation: A DDPG-based deep reinforcement learning ap- proach,” Available online: :arxiv.org/abs/2003.10923

    2003 arXiv

  74. [81]

    Deep reinforcement learning for unmanned aerial vehicle-assisted vehicular networks,

    M. Zhu, X.-Y . Liu, and X. Wang, “Deep reinforcement learning for unmanned aerial vehicle-assisted vehicular networks,”Available online: arxiv.org/abs/1906.05015

    1906 arXiv

  75. [82]

    Obstacle avoidance drone by deep reinforcement learning and its racing with human pilot,

    S.-Y . Shin, Y .-W. Kang, and Y .-G. Kim, “Obstacle avoidance drone by deep reinforcement learning and its racing with human pilot,” Applied Sciences, vol. 9, no. 24, p. 5571, 2019

    2019

  76. [83]

    Memory-based deep reinforcement learning for obstacle avoidance in UA V with limited environment knowledge,

    A. Singla, S. Padakandla, and S. Bhatnagar, “Memory-based deep reinforcement learning for obstacle avoidance in UA V with limited environment knowledge,”IEEE Transactions on Intelligent Transporta- tion Systems, 2019

    2019

  77. [85]

    Reward-driven U-net training for obstacle avoidance drone,

    S.-Y . Shin, Y .-W. Kang, and Y .-G. Kim, “Reward-driven U-net training for obstacle avoidance drone,” Expert Systems with Applications , vol. 143, p. 113064, 2020

    2020

  78. [86]

    A saliency-based reinforcement learning approach for a UA V to avoid flying obstacles,

    Z. Ma, C. Wang, Y . Niu, X. Wang, and L. Shen, “A saliency-based reinforcement learning approach for a UA V to avoid flying obstacles,” Robotics and Autonomous Systems , vol. 100, pp. 108–118, 2018

    2018

  79. [87]

    Intelligent decision-making for 3-dimensional dynamic obstacle avoidance of UA V based on deep reinforcement learning,

    X. Han, J. Wang, J. Xue, and Q. Zhang, “Intelligent decision-making for 3-dimensional dynamic obstacle avoidance of UA V based on deep reinforcement learning,” in Proc. 11th International Conference on Wireless Communications and Signal Processing (WCSP) . IEEE, 2019, pp. 1–6

    2019

  80. [88]

    Robust motion control for UA V in dynamic uncertain environments using deep reinforcement learning,

    K. Wan, X. Gao, Z. Hu, and G. Wu, “Robust motion control for UA V in dynamic uncertain environments using deep reinforcement learning,” Remote sensing, vol. 12, no. 4, p. 640, 2020

    2020

  81. [89]

    Deep reinforcement learning based local planner for UA V obstacle avoidance using demon- stration data,

    L. He, N. Aouf, J. F. Whidborne, and B. Song, “Deep reinforcement learning based local planner for UA V obstacle avoidance using demon- stration data,” Available online: arxiv.org/abs/2008.02521

    2008 arXiv

  82. [90]

    A deep reinforce- ment learning framework for UA V navigation in indoor environments,

    O. Walker, F. Vanegas, F. Gonzalez, and S. Koenig, “A deep reinforce- ment learning framework for UA V navigation in indoor environments,” in Proc. IEEE Aerospace Conference , 2019, pp. 1–14

    2019

  83. [91]

    Research on cooperative collision avoidance problem of multiple UA V based on reinforcement learning,

    F. Bin, F. XiaoFeng, and X. Shuo, “Research on cooperative collision avoidance problem of multiple UA V based on reinforcement learning,” in Proc. 10th International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2017, pp. 103–109

    2017

  84. [92]

    Uncertainty- aware reinforcement learning for collision avoidance,

    G. Kahn, A. Villaflor, V . Pong, P. Abbeel, and S. Levine, “Uncertainty- aware reinforcement learning for collision avoidance,” Available on- line: arxiv.org/abs/1702.01182

    arXiv

  85. [93]

    Online deep reinforcement learning for autonomous UA V navigation and exploration of outdoor environments,

    B. G. Maciel-Pearson, L. Marchegiani, S. Akcay, A. Atapour- Abarghouei, J. Garforth, and T. P. Breckon, “Online deep reinforcement learning for autonomous UA V navigation and exploration of outdoor environments,” Available online: arxiv.org/abs/1912.05684

    1912 arXiv

  86. [94]

    The multi-objective de- ployment optimization of UA V-mounted cache-enabled base stations,

    H. Dai, H. Zhang, B. Wang, and L. Yang, “The multi-objective de- ployment optimization of UA V-mounted cache-enabled base stations,” Physical Communication, vol. 34, pp. 114–120, 2019

    2019

  87. [95]

    A deep reinforcement learning strategy for UA V autonomous landing on a moving platform,

    A. Rodriguez-Ramos, C. Sampedro, H. Bavle, P. De La Puente, and P. Campoy, “A deep reinforcement learning strategy for UA V autonomous landing on a moving platform,” Journal of Intelligent & Robotic Systems, vol. 93, no. 1-2, pp. 351–366, 2019

    2019

  88. [96]

    Autonomous quadrotor landing using deep rein- forcement learning,

    R. Polvara, M. Patacchiola, S. Sharma, J. Wan, A. Manning, R. Sutton, and A. Cangelosi, “Autonomous quadrotor landing using deep rein- forcement learning,” Available online: arxiv.org/abs/1709.03339

    arXiv

  89. [97]

    A deep reinforcement learning technique for vision- based autonomous multirotor landing on a moving platform,

    A. Rodriguez-Ramos, C. Sampedro, H. Bavle, I. G. Moreno, and P. Campoy, “A deep reinforcement learning technique for vision- based autonomous multirotor landing on a moving platform,” in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp...

    2018

  90. [98]

    Toward end-to-end control for UA V autonomous landing via deep reinforcement learning,

    R. Polvara, M. Patacchiola, S. Sharma, J. Wan, A. Manning, R. Sutton, and A. Cangelosi, “Toward end-to-end control for UA V autonomous landing via deep reinforcement learning,” in Proc. International Con- ference on Unmanned Aircraft Systems (ICUAS) , 2018, pp. 115–123

    2018

  91. [99]

    Motor anomaly detection for unmanned aerial vehicles using reinforcement learning,

    H. Lu, Y . Li, S. Mu, D. Wang, H. Kim, and S. Serikawa, “Motor anomaly detection for unmanned aerial vehicles using reinforcement learning,” IEEE Internet of Things Journal , vol. 5, no. 4, pp. 2315– 2322, 2018

    2018

  92. [100]

    Interference management for cellular-connected UA Vs: A deep reinforcement learning approach,

    U. Challita, W. Saad, and C. Bettstetter, “Interference management for cellular-connected UA Vs: A deep reinforcement learning approach,” IEEE Transactions on Wireless Communications , vol. 18, no. 4, pp. 2125–2140, 2019

    2019

  93. [101]

    Trajectory optimization for autonomous flying base station via reinforcement learning,

    H. Bayerlein, P. De Kerret, and D. Gesbert, “Trajectory optimization for autonomous flying base station via reinforcement learning,” in Proc. IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018, pp. 1–5

    2018

  94. [102]

    Q- SQUARE: A Q-learning approach to provide a QoE aware UA V flight path in cellular networks,

    S. Colonnese, F. Cuomo, G. Pagliari, and L. Chiaraviglio, “Q- SQUARE: A Q-learning approach to provide a QoE aware UA V flight path in cellular networks,” Ad Hoc Networks, vol. 91, p. 101872, 2019

    2019

  95. [103]

    Obstacle avoidance path planning for UA V using reinforcement learning under simulated environment,

    I. Kim, S. Shin, J. Wu, S.-D. Kim, and C.-G. Kim, “Obstacle avoidance path planning for UA V using reinforcement learning under simulated environment,” in Proc. IASER 3rd International Conference on Elec- tronics, Electrical Engineering, Computer Science, Okinawa, 2017, pp. 34–36

    2017

  96. [104]

    Distributed drone base station positioning for emergency cellular networks using reinforcement learning,

    P. V . Klaine, J. P. Nadas, R. D. Souza, and M. A. Imran, “Distributed drone base station positioning for emergency cellular networks using reinforcement learning,” Cognitive computation , vol. 10, no. 5, pp. 790–804, 2018

    2018

  97. [105]

    Beyond empirical models: Pattern formation driven placement of UA V base stations,

    J. Lu, S. Wan, X. Chen, Z. Chen, P. Fan, and K. B. Letaief, “Beyond empirical models: Pattern formation driven placement of UA V base stations,” IEEE Transactions on Wireless Communications , vol. 17, no. 6, pp. 3641–3655, 2018

    2018

  98. [106]

    Reinforcement learning for decentral- ized trajectory design in cellular UA V networks with sense-and-send protocol,

    J. Hu, H. Zhang, and L. Song, “Reinforcement learning for decentral- ized trajectory design in cellular UA V networks with sense-and-send protocol,” IEEE Internet of Things Journal , vol. 6, no. 4, pp. 6177– 6189, 2018

    2018

  99. [107]

    Collaborative path planning based on MAXQ hierarchical reinforcement learning for manned/unmanned aerial vehicles,

    Y . Yan, H. Wang, and X. Chen, “Collaborative path planning based on MAXQ hierarchical reinforcement learning for manned/unmanned aerial vehicles,” in Proc. IEEE 39th Chinese Control Conference (CCC), 2020, pp. 4837–4842

    2020

  100. [108]

    Online maneuver design for UA V-enabled NOMA systems via reinforcement learning,

    Y . Huang, X. Mo, J. Xu, L. Qiu, and Y . Zeng, “Online maneuver design for UA V-enabled NOMA systems via reinforcement learning,” inProc. IEEE Wireless Communications and Networking Conference (WCNC) , 2020, pp. 1–6

    2020

  101. [109]

    Reinforcement learning-based collision avoidance and optimal trajectory planning in UA V communication networks,

    Y .-H. Hsu and R.-H. Gau, “Reinforcement learning-based collision avoidance and optimal trajectory planning in UA V communication networks,” IEEE Transactions on Mobile Computing , 2020

    2020

  102. [110]

    Autonomous tracking using a swarm of UA Vs: A constrained multi-agent reinforcement learning approach,

    Y .-J. Chen, D.-K. Chang, and C. Zhang, “Autonomous tracking using a swarm of UA Vs: A constrained multi-agent reinforcement learning approach,” IEEE Transactions on Vehicular Technology, 2020

    2020

  103. [111]

    Adaptive UA V-trajectory optimization under quality of service constraints: a model-free solution,

    J. Cui, Z. Ding, Y . Deng, A. Nallanathan, and L. Hanzo, “Adaptive UA V-trajectory optimization under quality of service constraints: a model-free solution,” IEEE Access, pp. 1–14, 2020

    2020

  104. [112]

    Au- tonomous UA V navigation using reinforcement learning,

    H. X. Pham, H. M. La, D. Feil-Seifer, and L. V . Nguyen, “Au- tonomous UA V navigation using reinforcement learning,” Available online: arxiv.org/abs/1801.05086

    arXiv

  105. [113]

    3D aerial base station position planning based on deep Q-network for capacity enhancement,

    J. Wu, P. Yu, L. Feng, F. Zhou, W. Li, and X. Qiu, “3D aerial base station position planning based on deep Q-network for capacity enhancement,” in IFIP/IEEE Symposium on Integrated Network and Service Management (IM) . IEEE, 2019, pp. 482–487

    2019

  106. [114]

    Cellular UA V-to-device communications: Trajectory design and mode selection by multi-agent deep reinforcement learning,

    F. Wu, H. Zhang, J. Wu, and L. Song, “Cellular UA V-to-device communications: Trajectory design and mode selection by multi-agent deep reinforcement learning,” IEEE Transactions on Communications , 2020

    2020

  107. [115]

    A reinforcement learning approach for fair user coverage using UA V mounted base stations under energy constraints,

    H. V . Abeywickrama, Y . He, E. Dutkiewicz, B. A. Jayawickrama, and M. Mueck, “A reinforcement learning approach for fair user coverage using UA V mounted base stations under energy constraints,” IEEE Open Journal of Vehicular Technology , vol. 1, pp. 67–81, 2020. 22

    2020

  108. [116]

    Joint flight cruise control and data collection in UA V-aided internet of things: An onboard deep reinforcement learning approach,

    K. Li, W. Ni, E. Tovar, and M. Guizani, “Joint flight cruise control and data collection in UA V-aided internet of things: An onboard deep reinforcement learning approach,” IEEE Internet of Things Journal , 2020

    2020

  109. [117]

    Maneuver decision of UA V in short-range air combat based on deep reinforcement learning,

    Q. Yang, J. Zhang, G. Shi, J. Hu, and Y . Wu, “Maneuver decision of UA V in short-range air combat based on deep reinforcement learning,” IEEE Access, vol. 8, pp. 363–378, 2019

    2019

  110. [120]

    An efficient deep reinforcement learning framework for UA Vs,

    S. Zhou, B. Li, C. Ding, L. Lu, and C. Ding, “An efficient deep reinforcement learning framework for UA Vs,” in Proc. IEEE 21st International Symposium on Quality Electronic Design (ISQED), 2020, pp. 323–328

    2020

  111. [121]

    Deep reinforcement learning based strategy for quadrotor UA V pur- suer and evader problem,

    D. Chen, Y . Wei, L. Wang, C. S. Hong, L.-C. Wang, and Z. Han, “Deep reinforcement learning based strategy for quadrotor UA V pur- suer and evader problem,” in Proc. IEEE International Conference on Communications Workshops (ICC Workshops), 2020, pp. 1–6

    2020

  112. [122]

    Path planning for UA V-mounted mobile edge computing with deep reinforcement learning,

    Q. Liu, L. Shi, L. Sun, J. Li, M. Ding, and F. Shu, “Path planning for UA V-mounted mobile edge computing with deep reinforcement learning,” IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5723–5728, 2020

    2020

  113. [123]

    Path planning for UA V ground target tracking via deep reinforcement learning,

    B. Li and Y . Wu, “Path planning for UA V ground target tracking via deep reinforcement learning,” IEEE Access, vol. 8, pp. 29 064–29 074, 2020

    2020

  114. [124]

    Deep reinforcement learning-based autonomous UA V navigation with sparse rewards,

    C. Wang, J. Wang, J. Wang, and X. Zhang, “Deep reinforcement learning-based autonomous UA V navigation with sparse rewards,” IEEE Internet of Things Journal , 2020

    2020

  115. [125]

    Cooperative internet of UA Vs: Distributed trajectory design by multi-agent deep reinforcement learning,

    J. Hu, H. Zhang, L. Song, R. Schober, and H. V . Poor, “Cooperative internet of UA Vs: Distributed trajectory design by multi-agent deep reinforcement learning,” IEEE Transactions on Communications, 2020

    2020

  116. [126]

    Multi- agent deep reinforcement learning based trajectory planning for multi- UA V assisted mobile edge computing,

    L. Wang, K. Wang, C. Pan, W. Xu, N. Aslam, and L. Hanzo, “Multi- agent deep reinforcement learning based trajectory planning for multi- UA V assisted mobile edge computing,”IEEE Transactions on Cognitive Communications and Networking , 2020

    2020

  117. [127]

    Distributed energy-efficient multi-UA V navigation for long-term communication coverage by deep reinforcement learning,

    C. H. Liu, X. Ma, X. Gao, and J. Tang, “Distributed energy-efficient multi-UA V navigation for long-term communication coverage by deep reinforcement learning,” IEEE Transactions on Mobile Computing , vol. 19, no. 6, pp. 1274–1285, 2019

    2019

  118. [128]

    Deep reinforcement learning for trajectory design and power allocation in UA V networks,

    N. Zhao, Y . Cheng, Y . Pei, Y .-C. Liang, and D. Niyato, “Deep reinforcement learning for trajectory design and power allocation in UA V networks,” inProc. IEEE International Conference on Commu- nications, Shanghai, May, 2019. , 2020, pp. 1–6

    2019

  119. [129]

    Three-dimensional obstacle avoidance for UA V based on reinforcement learning and RealSense,

    D. Han, Q. Yang, and R. Wang, “Three-dimensional obstacle avoidance for UA V based on reinforcement learning and RealSense,”The Journal of Engineering, vol. 2020, no. 13, pp. 540–544, 2020

    2020

  120. [130]

    3D UA V trajectory design and fre- quency band allocation for energy-efficient and fair communication: A deep reinforcement learning approach,

    R. Ding, F. Gao, and X. Shen, “3D UA V trajectory design and fre- quency band allocation for energy-efficient and fair communication: A deep reinforcement learning approach,” IEEE Transactions on Wireless Communications, 2020

    2020

  121. [131]

    Leveraging UA Vs for coverage in cell-free vehicular networks: A deep reinforcement learning approach,

    M. S. Shokry, D. Ebrahimi, C. Assi, S. Sharafeddine, and A. Ghrayeb, “Leveraging UA Vs for coverage in cell-free vehicular networks: A deep reinforcement learning approach,” IEEE Transactions on Mobile Computing, 2020

    2020

  122. [132]

    Energy-efficient UA V crowdsensing with multiple charging stations by deep learning,

    C. H. Liu, C. Piao, and J. Tang, “Energy-efficient UA V crowdsensing with multiple charging stations by deep learning,” in Proc. IEEE INFOCOM 2020-IEEE Conference on Computer Communications , 2020, pp. 199–208

    2020

  123. [133]

    Deep rein- forcement learning for trajectory design and power allocation in UA V networks,

    N. Zhao, Y . Cheng, Y . Pei, Y .-C. Liang, and D. Niyato, “Deep rein- forcement learning for trajectory design and power allocation in UA V networks,” inProc. IEEE International Conference on Communications (ICC), 2020, pp. 1–6

    2020

  124. [134]

    Reflections in the sky: Millimeter wave communication with uav-carried intelligent reflectors,

    Q. Zhang, W. Saad, and M. Bennis, “Reflections in the sky: Millimeter wave communication with uav-carried intelligent reflectors,” in Proc. IEEE Global Communications Conference (GLOBECOM) , 2019, pp. 1–6

    2019

  125. [135]

    Distributional reinforcement learning for mmwave communications with intelligent reflectors on a uav,

    Q. Zhang, W. Saad, and M. Bennis, “Distributional reinforcement learning for mmwave communications with intelligent reflectors on a uav,” Available online: arxiv.org/abs/2011.01840

    2011 arXiv

  126. [137]

    Op- timizing age of information through aerial reconfigurable intelligent surfaces: A deep reinforcement learning approach,

    M. Samir, M. Elhattab, C. Assi, S. Sharafeddine, and A. Ghrayeb, “Op- timizing age of information through aerial reconfigurable intelligent surfaces: A deep reinforcement learning approach,” Available online: arxiv.org/abs/2011.04817

    2011 arXiv

  127. [138]

    Joint trajectory and passive beamforming design for intelligent reflecting surface-aided uav communications: A deep reinforcement learning approach,

    L. Wang, K. Wang, C. Pan, W. Xu, and N. Aslam, “Joint trajectory and passive beamforming design for intelligent reflecting surface-aided uav communications: A deep reinforcement learning approach,” Available online: arxiv.org/abs/2007.08380

    2007 arXiv

  128. [139]

    A generic spatiotemporal scheduling for autonomous UA Vs: A reinforcement learning-based approach,

    O. Bouhamed, H. Ghazzai, H. Besbes, and Y . Massoud, “A generic spatiotemporal scheduling for autonomous UA Vs: A reinforcement learning-based approach,”IEEE Open Journal of Vehicular Technology, vol. 1, pp. 93–106, 2020

    2020

  129. [140]

    Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry,

    G. Faraci, A. Raciti, S. A. Rizzo, and G. Schembra, “Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry,” Applied Energy, vol. 259, p. 114204, 2020

    2020

  130. [141]

    Dynamic spectrum interaction of UA V flight formation communication with priority: A deep reinforcement learning approach,

    Y . Lin, M. Wang, X. Zhou, G. Ding, and S. Mao, “Dynamic spectrum interaction of UA V flight formation communication with priority: A deep reinforcement learning approach,” IEEE Transactions on Cogni- tive Communications and Networking , 2020

    2020

  131. [142]

    UA V- enabled computation migration for complex missions: A reinforcement learning approach,

    S. Zhu, L. Gui, N. Cheng, Q. Zhang, F. Sun, and X. Lang, “UA V- enabled computation migration for complex missions: A reinforcement learning approach,” IET Communications, 2020

    2020

  132. [143]

    Machine learning based edge- assisted UA V computation offloading for data analyzing,

    K. Kim, Y . M. Park, and C. S. Hong, “Machine learning based edge- assisted UA V computation offloading for data analyzing,” inProc. IEEE International Conference on Information Networking (ICOIN) , 2020, pp. 117–120

    2020

  133. [144]

    Design of a 5G network slice extension with MEC UA Vs managed with reinforcement learning,

    G. Faraci, C. Grasso, and G. Schembra, “Design of a 5G network slice extension with MEC UA Vs managed with reinforcement learning,” IEEE Journal on Selected Areas in Communications , vol. 38, no. 10, pp. 2356–2371, 2020

    2020

  134. [145]

    A reinforcement learning based user association algorithm for UA V networks,

    Q. Li, M. Ding, C. Ma, C. Liu, Z. Lin, and Y .-C. Liang, “A reinforcement learning based user association algorithm for UA V networks,” in Proc. 28th International Telecommunication Networks and Applications Conference (ITNAC) . IEEE, 2018, pp. 1–6

    2018

  135. [146]

    Application of reinforcement learning in uav cluster task scheduling,

    J. Yang, X. You, G. Wu, M. M. Hassan, A. Almogren, and J. Guna, “Application of reinforcement learning in uav cluster task scheduling,” Future Generation Computer Systems , vol. 95, pp. 140–148, 2019

    2019

  136. [147]

    Comparative study for coordinating multiple unmanned HAPS for communications area coverage,

    O. Anicho, P. B. Charlesworth, G. S. Baicher, A. Nagar, and N. Buck- ley, “Comparative study for coordinating multiple unmanned HAPS for communications area coverage,” in Proc. IEEE International Confer- ence on Unmanned Aircraft Systems (ICUAS) , 2019, pp. 467–474

    2019

  137. [148]

    Challenges of real-world reinforcement learning,

    G. Dulac-Arnold, D. Mankowitz, and T. Hester, “Challenges of real-world reinforcement learning,” Available online: arxiv.org/abs/1904.12901

    1904 arXiv

  138. [149]

    Evolution strategies as a scalable alternative to reinforcement learning,

    T. Salimans, J. Ho, X. Chen, S. Sidor, and I. Sutskever, “Evolution strategies as a scalable alternative to reinforcement learning,” Available online: arxiv.org/abs/1703.03864

    arXiv

  139. [150]

    Communication-efficient learning of deep networks from decentral- ized data,

    B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentral- ized data,” in Artificial Intelligence and Statistics , 2017, pp. 1273– 1282

    2017

  140. [151]

    Towards federated learning at scale: System design,

    K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V . Ivanov, C. Kiddon, J. Kone ˇcn`y, S. Mazzocchi, H. B. McMahan et al., “Towards federated learning at scale: System design,” Available online: arxiv.org/abs/1902.01046

    1902 arXiv

  141. [152]

    Federated learning in the sky: Joint power allocation and scheduling with UA V swarms,

    T. Zeng, O. Semiari, M. Mozaffari, M. Chen, W. Saad, and M. Bennis, “Federated learning in the sky: Joint power allocation and scheduling with UA V swarms,”Available online: arxiv.org/abs/2002.08196

    2002 arXiv

  142. [153]

    Federated learning for ultra-reliable low-latency V2V communications,

    S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Federated learning for ultra-reliable low-latency V2V communications,” in Proc. IEEE Global Communications Conference (GLOBECOM) , 2018, pp. 1–7

    2018

  143. [154]

    Distributed federated learning for ultra-reliable low-latency vehicular communications,

    ——, “Distributed federated learning for ultra-reliable low-latency vehicular communications,” IEEE Transactions on Communications , vol. 68, no. 2, pp. 1146–1159, 2019

    2019

  144. [155]

    Federated learning for task and resource allocation in wireless high altitude balloon networks,

    S. Wang, M. Chen, C. Yin, W. Saad, C. S. Hong, S. Cui, and H. V . Poor, “Federated learning for task and resource allocation in wireless high altitude balloon networks,” Available online: arxiv.org/abs/2003.09375

    2003 arXiv

  145. [156]

    Joint auction- coalition formation framework for communication-efficient federated learning in UA V-enabled internet of vehicles,

    J. Shyuan Ng, W. Y . B. Lim, H.-N. Dai, Z. Xiong, J. Huang, D. Niyato, X.-S. Hua, C. Leung, and C. Miao, “Joint auction- coalition formation framework for communication-efficient federated learning in UA V-enabled internet of vehicles,” Available online: arxiv.org/abs/2007.06378

    2007 arXiv

  146. [157]

    Communication-efficient massive UA V online path control: Federated learning meets mean-field game theory,

    H. Shiri, J. Park, and M. Bennis, “Communication-efficient massive UA V online path control: Federated learning meets mean-field game theory,” Available online: arxiv.org/abs/2003.04451

    2003 arXiv

  147. [158]

    Federated learning- based cognitive detection of jamming attack in flying ad-hoc network,

    N. I. Mowla, N. H. Tran, I. Doh, and K. Chae, “Federated learning- based cognitive detection of jamming attack in flying ad-hoc network,” IEEE Access, vol. 8, pp. 4338–4350, 2019. 23

    2019

  148. [159]

    AFRL: Adaptive federated reinforcement learning for intelli- gent jamming defense in FANET,

    ——, “AFRL: Adaptive federated reinforcement learning for intelli- gent jamming defense in FANET,” Journal of Communications and Networks, vol. 22, no. 3, pp. 244–258, 2020

    2020

  149. [160]

    HCP: Heterogeneous computing plat- form for federated learning based collaborative content caching towards 6G networks,

    Z. M. Fadlullah and N. Kato, “HCP: Heterogeneous computing plat- form for federated learning based collaborative content caching towards 6G networks,” IEEE Transactions on Emerging Topics in Computing , 2020

    2020

  150. [161]

    Federated learning in the sky: Aerial-ground air quality sensing framework with UA V swarms,

    Y . Liu, J. Nie, X. Li, S. H. Ahmed, W. Y . B. Lim, and C. Miao, “Federated learning in the sky: Aerial-ground air quality sensing framework with UA V swarms,”IEEE Internet of Things Journal, 2020

    2020

  151. [162]

    Towards federated learning in UA V-enabled internet of vehicles: A multi-dimensional contract-matching approach,

    B. Lim, J. Huang, Z. Xiong, J. Kang, D. Niyato, X.-S. Hua, C. Leung, and C. Miao, “Towards federated learning in UA V-enabled internet of vehicles: A multi-dimensional contract-matching approach,” Apr 2020

    2020

  152. [163]

    Practical secure ag- gregation for federated learning on user-held data,

    K. Bonawitz, V . Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure ag- gregation for federated learning on user-held data,” Available online: arxiv.org/abs/1611.044822

    arXiv

  153. [164]

    How to backdoor federated learning,

    E. Bagdasaryan, A. Veit, Y . Hua, D. Estrin, and V . Shmatikov, “How to backdoor federated learning,” in Proc. International Conference on Artificial Intelligence and Statistics . PMLR, 2020, pp. 2938–2948

    2020

  154. [165]

    Record and reward federated learning contributions with blockchain,

    I. Martinez, S. Francis, and A. S. Hafid, “Record and reward federated learning contributions with blockchain,” in Proc. IEEE International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2019, pp. 50–57

    2019

  155. [166]

    Blockchain-based node-aware dynamic weighting methods for improving federated learning performance,

    Y . J. Kim and C. S. Hong, “Blockchain-based node-aware dynamic weighting methods for improving federated learning performance,” in 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 2019, pp. 1–4

    2019

  156. [167]

    FLchain: Federated learning via MEC- enabled blockchain network,

    U. Majeed and C. S. Hong, “FLchain: Federated learning via MEC- enabled blockchain network,” in 20th Asia-Pacific Network Operations and Management Symposium (APNOMS) . IEEE, 2019, pp. 1–4

    2019

  157. [168]

    Blockchained on-device federated learning,

    H. Kim, J. Park, M. Bennis, and S.-L. Kim, “Blockchained on-device federated learning,” IEEE Communications Letters , vol. 24, no. 6, pp. 1279–1283, 2019

    2019

  158. [169]

    Blockchain and federated learning for privacy-preserved data sharing in industrial IoT,

    Y . Lu, X. Huang, Y . Dai, S. Maharjan, and Y . Zhang, “Blockchain and federated learning for privacy-preserved data sharing in industrial IoT,” IEEE Transactions on Industrial Informatics , vol. 16, no. 6, pp. 4177–4186, 2019

    2019

  159. [170]

    Decentralized privacy using blockchain-enabled federated learning in fog computing,

    Y . Qu, L. Gao, T. H. Luan, Y . Xiang, S. Yu, B. Li, and G. Zheng, “Decentralized privacy using blockchain-enabled federated learning in fog computing,” IEEE Internet of Things Journal , 2020

    2020

  160. [171]

    Learning in the air: Secure federated learning for UA V-assisted crowdsensing,

    Y . Wang, Z. Su, N. Zhang, and A. Benslimane, “Learning in the air: Secure federated learning for UA V-assisted crowdsensing,” IEEE Transactions on Network Science and Engineering , 2020

    2020

  161. [172]

    GIANT: Globally improved approximate newton method for distributed optimization,

    S. Wang, F. Roosta, P. Xu, and M. W. Mahoney, “GIANT: Globally improved approximate newton method for distributed optimization,” in Advances in Neural Information Processing Systems , 2018, pp. 2332– 2342

    2018

  162. [173]

    Federated learning: Strategies for improving communica- tion efficiency,

    J. Kone ˇcn`y, H. B. McMahan, F. X. Yu, P. Richt ´arik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communica- tion efficiency,” Available online: arxiv.org/abs/1610.05492

    arXiv

  163. [174]

    Cmfl: Mitigating communication overhead for federated learning,

    W. Luping, W. Wei, and L. Bo, “Cmfl: Mitigating communication overhead for federated learning,” in Proc. IEEE 39th International Conference on Distributed Computing Systems (ICDCS) , 2019, pp. 954–964

    2019

  164. [175]

    Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,

    S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,” Available online: arxiv.org/abs/1510.00149

    arXiv

  165. [176]

    Distributed mean estimation with limited communication,

    A. T. Suresh, X. Y . Felix, S. Kumar, and H. B. McMahan, “Distributed mean estimation with limited communication,” in Proc. International Conference on Machine Learning , 2017, pp. 3329–3337

    2017

  166. [177]

    Near- optimal straggler mitigation for distributed gradient methods,

    S. Li, S. M. M. Kalan, A. S. Avestimehr, and M. Soltanolkotabi, “Near- optimal straggler mitigation for distributed gradient methods,” in Proc. IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018, pp. 857–866. Mohamed-Amine Lahmeri (S'21) ...

    2018

Pith tools

Reviewed August 27, 2026 · model on record in the stance chip above.