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               REFERENCES
               1.   Nair A, Srinivasan P, Blackwell S, et al. Massively parallel methods for deep reinforcement learning. CoRR 2015;abs/
                   1507.04296. Available from: http://arxiv.org/abs/1507.04296.
               2.  Grounds M, Kudenko D. Parallel reinforcement learning with linear function approximation. In: Tuyls K, Nowe A, Guessoum Z,
                   Kudenko D, editors. Adaptive agents and multi­agent systems III. Adaptation and multi­agent learning. Berlin, Heidelberg: Springer
                   Berlin Heidelberg; 2008. pp. 60–74.
               3.  Clemente AV, Martínez HNC, Chandra A. Efficient parallel methods for deep reinforcement learning. CoRR 2017;abs/1705.04862.
                   Available from: http://arxiv.org/abs/1705.04862.
               4.   Lim WYB, Luong NC, Hoang DT, et al. Federated larning in mobile edge networks: A Comprehensive Survey.  IEEE
                   Communications Surveys Tutorials 2020;22:2031–63.
               5.   Nguyen DC, Ding M, Pathirana PN, et al. Federated learning for internet of things: a comprehensive survey. IEEE
                   Communications Surveys Tutorials 2021;23:1622–58.
               6.  Khan LU, Saad W, Han Z, Hossain E, Hong CS. Federated learning for internet of things: recent advances, taxonomy, and open
                   challenges. IEEE Communications Surveys Tutorials 2021;23:1759–99.
               7.  Yang Q, Liu Y, Cheng Y, et al. 1st ed. Morgan & Claypool; 2019.
               8.  Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: concept and applications. ACM T Intel Syst Tec 2019;10:1–19.
               9.  Qinbin L, Zeyi W, Bingsheng H. Federated learning systems: vision, hype and reality for data privacy and protection. CoRR
                   2019;abs/1907.09693. Available from: http://arxiv.org/abs/1907.09693.
               10.  Li T, Sahu AK, Talwalkar A, Smith V. Federated learning: challenges, methods, and future directions. IEEE Signal Process
                   Mag 2020;37:50–60.
               11.  Wang S, Tuor T, Salonidis T, Leung KK, et al. Adaptive federated learning in resource constrained edge computing systems.
                   IEEE J Sel Area Comm 2019;37:1205–21.
               12.  McMahan HB, Moore E, Ramage D, y Arcas BA. Communication­efficient learning of deep networks from decentralized data. CoRR
                   2016;abs/1602.05629. Available from: http://arxiv.org/abs/1602.05629.
               13.  Phong LT, Aono Y, Hayashi T, Wang L, Moriai S. Privacy­preserving deep learning via additively homomorphic encryption. IEEE
                   T Knowl Date En 2018;13:1333–45.
               14.  Zhu H, Jin Y. Multi­objective evolutionary federated learning. IEEE Transactions on Neural Networks and Learning Systems
                   2020;31:1310–22.
               15.  Kairouz P, McMahan HB, Avent B, et al.  Advances and open problems in federated learning.  CoRR 2019;abs/1912.04977.
                   Available from: http://arxiv.org/abs/1912.04977.
               16.  Pan SJ, Yang Q. A survey on transfer learning. IEEE T Knowl Date En 2010;22:1345–59.
               17.  Li Y. Deep reinforcement learning: an overview. CoRR 2017;abs/1701.07274. Available from: http://arxiv.org/abs/1701.07274.
               18.  Xu Z, Tang J, Meng J, et al. Experience­driven networking: a deep reinforcement learning based approach. In: IEEE INFOCOM 2018­
                   IEEE Conference on Computer Communications. IEEE; 2018. pp. 1871–79.
               19.  Mohammadi M, Al­Fuqaha A, Guizani M, Oh JS. Semisupervised deep reinforcement learning in support of IoT and smart city
                   services. IEEE Internet of Things Journal 2018;5:624–35. [DOI: 10.1109/JIOT.2017.2712560]
               20.  Bu F, Wang X. A smart agriculture IoT system based on deep reinforcement learning.  Future Generation Com
                   puter Systems 2019;99:500–507. Available from: https://www.sciencedirect.com/science/article/pii/S0167739X19307277.
               21.  Xiong X, Zheng K, Lei L, Hou L. Resource allocation based on deep reinforcement learning in IoT edge computing.  IEEE
                   J Sel Area Comm 2020;38:1133–46.
               22.  Lei L, Qi J, Zheng K. Patent analytics based on feature vector space model: a case of IoT. IEEE Access 2019;7:45705–15.
               23.  Shalev­Shwartz S, Shammah S, Shashua A.  Safe, Multi­Agent, Reinforcement Learning for Autonomous Driving.  CoRR
                   2016;abs/1610.03295. Available from: http://arxiv.org/abs/1610.03295.
               24.  Sallab AE, Abdou M, Perot E, Yogamani S. Deep reinforcement learning framework for autonomous driving. Electronic Imaging
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