Page 22 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 5 – Internet of Everything
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ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 5




          [18] M. A. Alsheikh, S. Lin, D. Niyato, and H. Tan. “Ma‑  [28] Z. Zhou, H. Liao, B. Gu, K. M. S. Huq, S. Mumtaz, and
               chine Learning in Wireless Sensor Networks: Al‑       J. Rodriguez. “Robust Mobile Crowd Sensing: When
               gorithms, Strategies, and Applications”. In: IEEE     Deep Learning Meets Edge Computing”. In: vol. 32.
               Communications Surveys Tutorials 16.4 (2014),         4. July 2018, pp. 54–60. DOI:10.1109/MNET.2018.
               pp. 1996–2018. ISSN:1553‑877X. DOI:10.1109/           1700442.
               COMST.2014.2320099.
                                                               [29]     Sangaiah,    V     T     M.    Hos‑
          [19] P. V. Klaine, M. A. Imran, O. Onireti, and R. D. Souza.  sain, and G. Muhammad. “Enforcing Position‑Based
               “A Survey of Machine Learning Techniques Applied        identiality        Paradigm
               to Self‑Organizing Cellular Networks”. In: IEEE       through Mobile Edge Computing in Real‑Time In‑
                                                                     dustrial Informatics”. In: IEEE Transactions on In‑
               Communications Surveys Tutorials 19.4 (2017),
               pp. 2392–2431. ISSN:1553‑877X. DOI:10.1109/           dustrial  Informatics  (2019),    1–1.  ISSN:  1551‑
               COMST.2017.2727878.                                   3203. DOI: 10.1109/TII.2019.2898174.
          [20] H. B. McMahan, E. Moore, D. Ramage, and B. Agüera  [30]    Yu,    Wang,      Langar  “Computation  of‑
               y Arcas. “Federated Learning of Deep Networks          loading for mobile edge computing: A deep learn‑
               using Model Averaging”. In: vol. abs/1602.05629.      ing approach”. In: 2017 IEEE 28th Annual Interna‑
               2016. arXiv: 1602.05629. URL:http:/ /arxiv.           tional  Symposium  on  Personal,  Indoor,  and  Mobile
               org/abs/1602.05629.                                   Radio  Communications  (PIMRC).  Oct.  2017,
                                                                     pp. 1–6. DOI: 10.1109/PIMRC.2017.8292514.
          [21] Q. Yang, Y. Liu, T. Chen, and Y. Tong. “Federated
                                                               [31] N. C. Luong, Z. Xiong, P . Wang, and D. Niyato. “Op‑
               Machine Learning: Concept and Applications”. In:
                                                                     timal Auction for Edge Computing Resource Man‑
               vol. abs/1902.04885. 2019. arXiv: 1902 . 04885.
                                                                     agement    Mobile  Blockchain  Networks:    Deep
               URL:http://arxiv.org/abs/1902.04885.
                                                                     Learning Approach”. In: (May 2018), pp. 1–6. ISSN:
          [22] V. Smith, C.‑K. Chiang, M. Sanjabi, and A. Tal‑
                                                                     1938-1883. DOI: 10.1109/ICC.2018.8422743.
               walkar. “Federated Multi‑Task Learning”. In:
                                                               [32] T . Tuor, S. Wang, T . Salonidis, B. J. Ko, and K. K. Le‑
               vol. abs/1705.10467. 2017. arXiv: 1705 . 10467.
                                                                     ung. “Demo abstract: Distributed machine learning
               URL:http://arxiv.org/abs/1705.10467.
                                                                     at resource‑limited edge nodes”. In: IEEE INFOCOM
          [23] N. H. Tran, W. Bao, A. Zomaya, N. Minh N.H., and      2018  ‑  IEEE  Conference  on  Computer  Communica‑
               C. S. Hong. “Federated Learning over Wireless Net‑
                                                                     tions  Workshops  (INFOCOM  WKSHPS)  Apr  2018,
               works: Optimization Model Design and Analysis”.       pp. 1–2. DOI: 10.1109/INFCOMW.2018.8406837.
               In: IEEE INFOCOM 2019 ‑ IEEE Conference on Com‑
                                                               [33] T       T   Ohtsuki.  “Cell      dis‑
               puter Communications. Apr. 2019, pp. 1387–1395.
                                                                       Q‑learning    heterogeneous  networks”.
               DOI:10.1109/INFOCOM.2019.8737464.
                                                                       2013    ic  Signal  and  Information  Pro‑
          [24] Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, and H.
                                                                     cessing Association Annual Summit and Conference.
               Qi. “Beyond Inferring Class Representatives: User‑
                                                                     Oct. 2013, pp. 1–6. DOI: 10.1109/APSIPA.2013.
               Level Privacy Leakage From Federated Learning”.       6694368.
               In: IEEE INFOCOM 2019 ‑ IEEE Conference on Com‑
                                                               [34]   Li,    Ota,        “Human      Loop:
               puter Communications. Apr. 2019, pp. 2512–2520.
                                                                     Distributed Deep Model for Mobile Crowdsensing”.
               DOI:10.1109/INFOCOM.2019.8737416.
                                                                     In: IEEE Internet of Things Journal 5.6 (Dec. 2018),
          [25] H. B. McMahan, E. Moore, D. Ramage, and B.            pp. 4957–4964. ISSN: 2327‑4662. DOI: 10 . 1109 /
               Agüera y Arcas. “Communication‑Ef icient Learn‑
                                                                     JIOT.2018.2883318.
               ing of Deep Networks from Decentralized Data”.
               In: Proceedings of the 20th International Conference  [35]   Valerio,    Passarella,        “Optimal
               on Arti icial Intelligence and Statistics. Vol. 54. Pro‑  trade‑off  between          of
               ceedings of Machine Learning Research. Fort Laud‑     distributed learning in Mobile Edge Computing: An
               erdale, FL, USA: PMLR, Apr. 2017, pp. 1273–1282.      analytical approach”. In: (June 2017), pp. 1–9. DOI:
               URL:http : / / proceedings . mlr . press / v54 /      10.1109/WoWMoM.2017.7974310.
               mcmahan17a.html.                                [36] Y . Jiao, P . Wang, D. Niyato, M. Abu Alsheikh, and S.
          [26] P. Subramaniam and M. J. Kaur. “Review of Security    Feng. “Pro it Maximization Auction and Data Man‑
               in Mobile Edge Computing with Deep Learning”. In:     agement in Big Data Markets”. In: 2017 IEEE Wire‑
               (Mar. 2019), pp. 1–5. DOI:10.1109/ICASET.2019.        less  Communications  and  Networking  Conference
                                                                     (WCNC). Mar. 2017, pp. 1–6. DOI: 10.1109/WCNC.
               8714349.
                                                                     2017.7925760.
          [27] Z. Chang, L. Lei, Z. Zhou, S. Mao, and T. Ristaniemi.
               Learn to Cache: Machine Learning for Network Edge
               Caching in the Big Data Era. Vol. 25. 3. June 2018,
               pp. 28–35. DOI:10.1109/MWC.2018.1700317.






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