Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

IEEE International Conference on Communications

Volumn 2019-May, Issue , 2019, Pages

  Nishio, Takayuki ^a   Yonetani, Ryo ^b  

^a KYOTO UNIVERSITY   (Japan)

^b Nagase Hongo Building 3F   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

AGGREGATES; LARGE DATASET;

COMPUTATION RESOURCES; COMPUTATIONAL RESOURCES; DECENTRALIZED LEARNING; ENVIRONMENT SIMULATION; EXPERIMENTAL EVALUATION; HETEROGENEOUS RESOURCES; LARGE-SCALE IMAGE DATASETS; WIRELESS CHANNEL CONDITION;

DEEP NEURAL NETWORKS;

EID: 85070206410     PISSN: 15503607     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICC.2019.8761315     Document Type: Conference Paper

References (20)

1. J. Choi , V. Va, N. Gon zalez-Prel cic, R. Daniels, C. R. Bhat, and R. W. Heath , "Millimeter-wave vehicular communication to support massive automotive sensing, " IEEE Commun. Mag., vol. 54, no. 12, pp. 160167, Dec. 2016.
2. Y.C. Hu, M. Patel , D. Sabella , N. Sprecher, and V.Young, "Mobile edge com puting-A key technology towards 5G, " ETS I white paper, 2015.
3. X. Liu, Y. Liu, H. Song, and A. Liu, "Big data orche stration as a service network, " IEEE Commun. Mag., vol. 55, no. 9, pp. 94-10 1, Sept. 2017.
4. S. Kato and R. Shinkuma, "Priority contro l in communication networks for accuracy-freshness tradeoff in real-time road-traffic information delivery, " IEEE Access, vol. 5, pp. 25 226-25235, Oct. 2017.
5. H. B. McMah an, E. Moore , D. Ramage, S. Hampson, and B. A. YArea s, "Communication-efficient learnin g of deep networks from decent ralized data, " in Proc. of the International Confe rence on Artificial Intelligence and Statistics, Apr. 20 17.
6. T. Nishio, R. Shinkuma, T. Takahashi, and N. B. Manda yam, "Serviceoriented heterogeneous reso urce sharing for optimizing service latenc y in mobile cloud , " in Proc. of the International Workshop on Mobile Cloud Computing & Networking, July 2013, pp. 19-26.
7. S. Sardel litti , G. Scutari , and S. Barbarossa, "Joint optimi zation of radio and computational resources for multicell mobile-edge computing, " IEEE Transactions on Signal and Information Processing over Networks, vol. I, no. 2, pp. 89-103, June 2015.
8. Y. Yu, J. Zhang, and K. B. Letaie f, "Joint subcarrier and CPU time allo cation for mobile edge computing, " in Proc. of the IEEE Globecom, Dec. 20 16, pp. 1-6.
9. K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone , H. B. McMah an, S. Patel , D. Ramage, A. Segal, and K. Seth, "Practical secure aggr egation for privacy-pre serving machine learnin g, " in Proc. of the ACM CCS, Oct. 2017 , pp. 1175-1191.
10. R. C. Geyer, T. Klein, and M. Nabi, "Differentially private federa ted learnin g: A client level perspectiv e, " CaRR, vol. abs/1712.07557, 2017.
11. J. Konecn y, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon, "Federated learning: Strate gies for impro ving communication efficiency, " in Proc. of the NIPS Workshop on Private Multi-Party Machine Learning, Dec. 2016.
12. S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, "When edge meets learnin g: Adapti ve cont rol for resource constrained distributed machin e learnin g, " in Proc. of the IEEE INFOCOM, Apr. 20 18.
13. S. Sesia, M. Baker, and I. Toufik, LTE-The UMTS Long Term Evolution: From Theory to Practice. John Wiley & Sons, 2011.
14. M. Sviridenko, "A note on maximi zing a submodular set function subject to a knapsack constraint, " Operations Research Letters, vol. 32, no. I, pp. 41-43, Jan. 2004.
15. M. Serie s, "Guidelines for evaluation of radio interface technologies for IMT-Advanced, " Report ITU-R M.2 135-I, 2009.
16. M. R. Akdeni z, Y. Liu, M. K. Samimi, S. Sun, S. Rangan, T. S. Rappaport , and E. Erkip, "Millimeter wave channel modeli ng and cellular capacity evaluation, " IEEE J. Set. Areas Commun., vol. 32, no. 6, pp. 1164-11 79, June 2014.
17. H. Xiao, K. Rasul , and R. Vollgraf, "Fas hion-MNIST: A novel image dataset for benchmarking machine learning algorithms, " CaRR, vol. absIl708.07747, 2017.
18. Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, "Federated learnin g with non-iid data, " CaRR, vol. absIl806.00582, 20 18.
19. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learnin g for image recognition, " in Proc. of the IEEE CVPR, Dec. 2016, pp. 171-180.
20. Y. Yamada, M. Iwamura, and K. Kise, "Shakedrop regulari zation, " CaRR, vol. abs1l8 02.0237 5, 2018.