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In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a neural network by communicating learning updates with an access point without exchanging their data samples.
M. Ergen, S. Coleri, and P. Varaiya, “QoS aware adaptive resource allocation techniques for fair scheduling in OFDMA based broadband wireless access systems,” IEEE Trans. Broadcast. , vol. 49, no. 4, pp. 362-370, Dec. 2003
2003
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Convex Optimization . Cambridge, U.K.: Cambridge Univ. Press, 2004
2004
Earlier work this paper cites.
2004
Earlier work this paper cites.
Z. Shen, J. G. Andrews, and B. L. Evans, “Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints,” IEEE Trans. Wireless Commun. , vol. 4, no. 6, pp. 2726-2737, Nov. 2005
2005
Earlier work this paper cites.
P. Zhao and T. Zhang, “Stochastic optimization with importance sampling for regularized loss minimization,” in Proc. 32nd Int. Conf. Mach. Learn. (ICML) , Lille, France, Jul. 2015, pp. 1–9
2015
Earlier work this paper cites.
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” in Proc. of NeurIPS Workshop on Private Multi-Party Machine Learning , Barcelona, Spain, Dec. 2016, pp. 1-5
2016
Earlier work this paper cites.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Proc. Int. Conf. Artif. Intell. Statist. (AISTATS) , Fort Lauderdale, FL, USA, Apr. 2017, pp. 1-10
2017
Earlier work this paper cites.
Radio Frequency (RF) System Scenarios , document 3GPP TR 25.942, v.14.0.0, 2017
2017
Earlier work this paper cites.
Q. Mao, F. Hu, and Q. Hao, “Deep learning for intelligent wireless networks: A comprehensive survey,” IEEE Commun. Surv. Tut. , vol. 20, no. 4, pp. 2595-2621, Fourthquarter 2018
2018
Earlier work this paper cites.
T. Chen, G. B. Giannakis, T. Sun, and W. Yin, “Lag: Lazily aggregated gradient for communication-efficient distributed learning,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) , Montreal, QC, Canada, Dec. 2018, pp. 5050-5060
2018
Earlier work this paper cites.
W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” to appear in IEEE Network, 2019
2019
Earlier work this paper cites.
M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial neural networks-based machine learning for wireless networks: A tutorial,” IEEE Commun. Surv. Tut. , vol. 21, no. 4, pp. 3039-3071, Fourthquarter 2019
2019
Earlier work this paper cites.
C. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless networking: A survey,” IEEE Commun. Surv. Tut. , vol. 21, no. 3, pp. 2224-2287, Thirdquarter 2019
2019
Earlier work this paper cites.
X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, “In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning,” IEEE Network , vol. 33, no. 5, pp. 156-165, Sep. 2019
2019
Cited alongside, same era.
J. Park, S. Samarakoon, M. Bennis, and M. Debbah, “Wireless network intelligence at the edge,” Proceedings of the IEEE, vol. 107, no. 11, pp. 2204-2239, Nov. 2019
2019
Cited alongside, same era.
T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge,” in Proc. IEEE Int. Conf. Commun. (ICC), Shanghai, China, May 2019, pp. 1–7
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
G. Zhu, D. Liu, Y. Du, C. You, J. Zhang, and K. Huang, “Towards an intelligent edge: Wireless communication meets machine learning,” IEEE Commun. Mag., vol. 58, no. 1, pp. 19-25, Jan. 2020
2020
Closest in time.
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2019
Cited alongside, same era.
D. Wen, X. Li, Q. Zeng, J. Ren, and K. Huang, “An overview of data-importance aware radio resource management for edge machine learning”, J. Commun. Inform. Netw, vol. 4, no. 4, pp. 1-14, Dec. 2019
2019
Cited alongside, same era.
F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Sparse binary compression: Towards distributed deep learning with minimal communication,” in Proc. Int. Joint Conf. Neural Networks (IJCNN) , Budapest, Hungary, Jul. 2019, pp. 1-8
2019
Cited alongside, same era.
Y. Lin, S. Han, H. Mao, Y. Wang, and W. J. Dally, “Deep gradient compression: Reducing the communication bandwidth for distributed training,” in Proc. Int. Joint Conf. Neural Networks (IJCNN) , Budapest, Hungary, Jul. 2019, pp. 1-8
2019
Cited alongside, same era.
S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” IEEE J. Sel. Areas Commun., vol. 37, no. 6, pp. 1205-1221, Jun. 2019
2019
Cited alongside, same era.
M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, “Performance optimization of federated learning over wireless networks,” in Proc. IEEE Global Commun. Conf. (GLOBECOM) , Waikoloa, HI, USA, 2019, pp. 1-6
2019
Cited alongside, same era.
N. H. Tran, W. Bao, A. Zomaya, N. Minh N.H., and C. S. Hong, “Federated learning over wireless networks: Optimization model design and analysis,” in Proc. IEEE Int. Conf. Comput. Comnun. (INFOCOM) , Paris, France, 2019, pp. 1387-1395
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Z. Zhao, C. Feng, H. H. Yang, and X. Luo, “Federated-learning-enabled intelligent fog radio access networks: Fundamental theory, key techniques, and future trends,” IEEE Wireless Commun. Mag. , vol. 27, no. 2, pp. 22-28, Apr. 2020
2020
Closest in time.
H. H. Yang, A. Arafa, T. Q. S. Quek, and H. V. Poor, “Age-based scheduling policy for federated learning in mobile edge networks,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Barcelona, Spain, May 2020, pp. 8743-8747
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning via over-the-air computation,” IEEE Trans. Wireless Commun. , vol. 19, no. 3, pp. 2022-2035, Mar. 2020
2020
Closest in time.
G. Zhu, Y. Wang, and K. Huang, “Broadband analog aggregation for low-latency federated edge learning,” IEEE Trans. Wireless Commun. , vol. 19, no. 1, pp. 491-506, Jan. 2020
2020
Closest in time.
2020
Closest in time.
H. H. Yang, Z. Liu, T. Q. S. Quek, and H. V. Poor, “Scheduling policies for federated learning in wireless networks,” IEEE Trans. Commun. , vol. 68, no. 1, pp. 317-333, Jan. 2020
2020
Closest in time.