Page 62 - Read Online
P. 62
Page 57 Qi et al. Intell Robot 2021;1(1):18-57 I http://dx.doi.org/10.20517/ir.2021.02
104. Liang X, Liu Y, Chen T, Liu M, Yang Q. Federated transfer reinforcement learning for autonomous driving. arXiv:191006001 [cs]
2019 Oct. ArXiv: 1910.06001. Available from: http://arxiv.org/abs/1910.06001.
105. Lim HK, Kim JB, Heo JS, Han YH. Federated reinforcement learning for training control policies on multiple IoT devices. Sensors
2020 Mar;20:1359. Available from: https://www.mdpi.com/14248220/20/5/1359.
106. Lim HK, Kim JB, Ullah I, Heo JS, Han YH. Federated reinforcement learning acceleration method for precise control of multiple
devices. IEEE Access 2021;9:76296–306.
107. Mowla NI, Tran NH, Doh I, Chae K. AFRL: adaptive federated reinforcement learning for intelligent jamming defense in FANET.
Journal of Communications and Networks 2020;22:244–58.
108. Nguyen TG, Phan TV, Hoang DT, Nguyen TN, SoIn C. Federated deep reinforcement learning for traffic monitoring in SDNBased
IoT networks. IEEE T Cogn Commun 2021:1–1.
109. Wang X, Garg S, Lin H, et al. Towards accurate anomaly detection in industrial internetofthings using hierarchical federated
learning. IEEE Internet Things 2021:1–1.
110. Lee S, Choi DH. Federated reinforcement learning for energy management of multiple smart homes with distributed energy re
sources. IEEE T Ind Inform 2020:1–1.
111. Samet H. The quadtree and related hierarchical data structures. ACM Comput Surv 1984;16:187–260. Available from: https: //
doi.org/10.1145/356924.356930.
112. AbdelAziz MK, Samarakoon S, Perfecto C, Bennis M. Cooperative perception in vehicular networks using multiagent re
inforcement learning. In: 2020 54th Asilomar Conference on Signals, Systems, and Computers; 2020. pp. 408–12.
113. Wang H, Kaplan Z, Niu D, Li B. Optimizing federated learning on nonIID data with reinforcement learning. In: IEEE INFOCOM
2020 IEEE Conference on Computer Communications. Toronto, ON, Canada: IEEE; 2020. pp. 1698–707. Available from: https:
//ieeexplore.ieee.org/document/9155494/.
114. Zhang P, Gan P, Aujla GS, Batth RS. Reinforcement learning for edge device selection using social attribute perception in industry
4.0. IEEE Internet Things 2021:1–1.
115. Zhan Y, Li P, Leijie W, Guo S. L4L: Experiencedriven computational resource control in federated learning. IEEE T Comput
2021:1–1.
116. Dong Y, Gan P, Aujla GS, Zhang P. RARL: reputationaware edge device selection method based on reinforcement learning. In:
2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM); 2021. pp. 348–53.
117. Sahu AK, Li T, Sanjabi M, et al. On the convergence of federated optimization in heterogeneous networks. CoRR 2018;abs/
1812.06127. Available from: http://arxiv.org/abs/1812.06127.
118. Chen M, Poor HV, Saad W, Cui S. Convergence time optimization for federated learning over wireless networks. IEEE
T Wirel Commun 2021;20:2457–71.
119. Li X, Huang K, Yang W, Wang S, Zhang Z. On the convergence of FedAvg on nonIID data; 2020. Available from: https://arxiv.org/
abs/1907.02189?context=stat.ML.
120. Bonawitz KA, Eichner H, Grieskamp W, et al. Towards federated learning at scale: system design. CoRR 2019;abs/1902.01046.
Available from: http://arxiv.org/abs/1902.01046.
121. Mnih V, Kavukcuoglu K, Silver D, et al. Humanlevel control through deep reinforcement learning. Nature 2015;518:529–33.
Available from: https://doi.org/10.1038/nature14236.
122. Lillicrap TP, Hunt JJ, Pritzel A, et al. Continuous control with deep reinforcement learning; 2019. Available from: https://arxiv.org/
abs/1509.02971.
123. Lyu L, Yu H, Yang Q. Threats to federated learning: a survey. CoRR 2020;abs/2003.02133. Available from: https://arxiv.org/abs/20
03.02133.
124. Fung C, Yoon CJM, Beschastnikh I. Mitigating sybils in federated learning poisoning. CoRR 2018;abs/1808.04866. Available from:
http://arxiv.org/abs/1808.04866.
125. Anwar A, Raychowdhury A. Multitask federated reinforcement learning with adversaries; 2021.
126. Zhu L, Liu Z, Han S. Deep leakage from gradients. CoRR 2019;abs/1906.08935. Available from: http://arxiv.org/abs/1906.08935.
127. Nishio T, Yonetani R. Client selection for federated learning with heterogeneous resources in mobile edge. In: ICC 2019 2019
IEEE International Conference on Communications (ICC); 2019. pp. 1–7.
128. Yang T, Andrew G, Eichner H, et al. Applied federated learning: improving Google Keyboard query suggestions. CoRR 2018;abs/
1812.02903. Available from: http://arxiv.org/abs/1812.02903.
129. Yu H, Liu Z, Liu Y, et al. A fairnessaware incentive scheme for federated learning. In: Proceedings of the AAAI/ACM
Conference on AI, Ethics, and Society. AIES ’20. New York, NY, USA: Association for Computing Machinery; 2020. p. 393–399.
Available from: https://doi.org/10.1145/3375627.3375840.