Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data

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H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,” CoRR , vol. abs/1708.07747, Sep. 2017
Original
1905
Earlier work this paper cites.
M. Series, “Guidelines for evaluation of radio interface technologies for IMT-Advanced,” Report ITU-R M.2135-1, 2009
2009
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Technical report, Univ. of Toronto, 2009
2009
Earlier work this paper cites.
L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proc. COMPSTAT 2010 , Y. Lechevallier and G. Saporta, Eds., Heidelberg, Germany, Aug. 2010, pp. 177–186
2010
Earlier work this paper cites.
S. Sesia, M. Baker, and I. Toufik, LTE-the UMTS long term evolution: from theory to practice . John Wiley & Sons, 2011
2011
Earlier work this paper cites.

T. Nishio, R. Shinkuma, T. Takahashi, and N. B. Mandayam, “Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud,” in Proc. First International Workshop on Mobile Cloud Computing & Networking , Bangalore, India, 2013, pp. 19–26
2013
Cited alongside, same era.
M. R. Akdeniz, Y. Liu, M. K. Samimi, S. Sun, S. Rangan, T. S. Rappaport, and E. Erkip, “Millimeter wave channel modeling and cellular capacity evaluation,” IEEE J. Sel. Areas Commun. , vol. 32, no. 6, pp. 1164–1179, Jun. 2014
2014
Cited alongside, same era.
D. Zeng, L. Gu, S. Guo, Z. Cheng, and S. Yu, “Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system,” IEEE Trans. Computers , vol. 65, no. 12, pp. 3702–3712, Dec. 2016
2016
Cited alongside, same era.
J. Konecný, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” in Proc. NIPS Workshop on Private Multi-Party Machine Learning , Dec. 2016

Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-IID data,” CoRR , vol. abs/1806.00582, Jun. 2018
Original
2018
Later among the works it cites.
T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge,” in Proc. IEEE ICC , May 2019, pp. 1–7
2019
Closest in time.
F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-iid data,” CoRR , vol. abs/1903.02891, Mar. 2019
Original
2019
Closest in time.
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
Closest in time.

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Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data · Around

H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,” CoRR , vol. abs/1708.07747, Sep. 2017
Original
1905
Earlier work this paper cites.
M. Series, “Guidelines for evaluation of radio interface technologies for IMT-Advanced,” Report ITU-R M.2135-1, 2009
2009
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Technical report, Univ. of Toronto, 2009
2009
Earlier work this paper cites.
L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proc. COMPSTAT 2010 , Y. Lechevallier and G. Saporta, Eds., Heidelberg, Germany, Aug. 2010, pp. 177–186
2010
Earlier work this paper cites.
S. Sesia, M. Baker, and I. Toufik, LTE-the UMTS long term evolution: from theory to practice . John Wiley & Sons, 2011
2011
Earlier work this paper cites.

T. Nishio, R. Shinkuma, T. Takahashi, and N. B. Mandayam, “Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud,” in Proc. First International Workshop on Mobile Cloud Computing & Networking , Bangalore, India, 2013, pp. 19–26
2013
Cited alongside, same era.
M. R. Akdeniz, Y. Liu, M. K. Samimi, S. Sun, S. Rangan, T. S. Rappaport, and E. Erkip, “Millimeter wave channel modeling and cellular capacity evaluation,” IEEE J. Sel. Areas Commun. , vol. 32, no. 6, pp. 1164–1179, Jun. 2014
2014
Cited alongside, same era.
D. Zeng, L. Gu, S. Guo, Z. Cheng, and S. Yu, “Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system,” IEEE Trans. Computers , vol. 65, no. 12, pp. 3702–3712, Dec. 2016
2016
Cited alongside, same era.
J. Konecný, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” in Proc. NIPS Workshop on Private Multi-Party Machine Learning , Dec. 2016

Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-IID data,” CoRR , vol. abs/1806.00582, Jun. 2018
Original
2018
Later among the works it cites.
T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge,” in Proc. IEEE ICC , May 2019, pp. 1–7
2019
Closest in time.
F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-iid data,” CoRR , vol. abs/1903.02891, Mar. 2019
Original
2019
Closest in time.
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
Closest in time.

alphaXiv searches the wider corpus for related work and actual follow-ups.

alphaXiv is searching for related work…