Fetching the paper…
Reading the bibliography…
This study develops a federated learning (FL) framework overcoming largely incremental communication costs due to model sizes in typical frameworks without compromising model performance.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE , vol. 86, no. 11, pp. 2278–2324, Nov. 1998
1998
Earlier work this paper cites.
D. D. Lewis et al. , “Reuters-21578,” http://www.daviddlewis.com/resources/testcollections/reuters21578
2004
Earlier work this paper cites.
A. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proc. ACL HLT , Portland, Oregon, USA, Jun. 2011, pp. 142–150
2011
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” in Proc. NeurIPS Wksp. Deep Learning , Montreal, Canada, Dec. 2014, pp. 1–9
2014
Earlier work this paper cites.
F. Chollet et al. , “Keras,” https://keras.io
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. NeurIPS Wksp. PMPML , Barcelona, Spain, Dec. 2016, pp. 1–10
2016
Earlier work this paper cites.
F. Li, B. Zhang, and B. Liu, “Ternary weight networks,” in Proc. NeurIPS , Barcelona, Spain, Dec. 2016, pp. 1–5
2016
Earlier work this paper cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016, http://www.deeplearningbook.org
2016
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Proc. AISTATS , Fort Lauderdale, FL, USA, Apr. 2017, pp. 1273–1282
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar, “Semi-supervised knowledge transfer for deep learning from private training data,” in Proc. ICLR , Toulon, France, Apr. 2017, pp. 1–16
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
E. Jeong, S. Oh, H. Kim, J. Park, M. Bennis, and S.-L. Kim, “Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data,” in Proc. NeurIPS Wksp. MLPCD , Montreal, Canada, Nov. 2018
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
J. Bernstein, Y.-X. Wang, K. Azizzadenesheli, and A. Anandkumar, “signSGD: Compressed optimisation for non-convex problems,” in Proc. ICMR , Stockholm, Sweden, Aug. 2018, pp. 1–25
2018
Cited alongside, same era.
T. Chen, G. Giannakis, T. Sun, and W. Yin, “LAG: Lazily aggregated gradient for communication-efficient distributed learning,” in Proc. NeurIPS , Montreal, Canada, Dec. 2018, pp. 5050–5060
2018
Cited alongside, same era.
R. Anil, G. Pereyra, A. Passos, R. Ormandi, G. Dahl, and G. Hinton, “Large scale distributed neural network training through online distillation,” in Proc. ICLR , Vancouver, Canada, May 2018, pp. 1–12
2018
Cited alongside, same era.
W. Y. B. Lim, N. C. Luong, D. T. Hoang, Y. Jiao, Y. C. Liang, Q. Yang, D. Niyato, and C. Miao, “Federated learning in mobile edge networks: A comprehensive survey,” IEEE Commun. Surv. Tutor. , vol. 22, no. 3, pp. 2031–2063, Apr. 2020
2020
Closest in time.
S. Oh, J. Park, E. Jeong, H. Kim, M. Bennis, and S. L. Kim, “Mix2fld: Downlink federated learning after uplink federated distillation with two-way mixup,” IEEE Commun. Lett. , vol. 24, no. 10, pp. 2211–2215, Oct. 2020
2020
Closest in time.
——, “Cooperative learning via federated distillation over fading channels,” in Proc. IEEE ICASSP , Barcelona, Spain, May 2020, pp. 8856–8860
2020
Closest in time.
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Zhang, T. Xiang, T. M. Hospedales, and H. Lu, “Deep mutual learning,” in Proc. IEEE CVPR , Salt Lake City, Utah, USA, Jun. 2018, pp. 4320–4328
2018
Cited alongside, same era.
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and Ú. Erlingsson, “Scalable private learning with PATE,” in Proc. ICLR , Vancouver, Canada, May 2018, pp. 1–34
2018
Cited alongside, same era.
R. Zhao and K. Mao, “Fuzzy bag-of-words model for document representation,” IEEE Trans. Fuzzy Syst. , vol. 26, no. 2, pp. 794–804, 2018
2018
Cited alongside, same era.
J. Park, S. Samarakoon, M. Bennis, and M. Debbah, “Wireless network intelligence at the edge,” Proc. IEEE , vol. 107, no. 11, pp. 2204–2239, Nov. 2019
2019
Cited alongside, same era.
J.-H. Ahn, O. Simeone, and J. Kang, “Wireless federated distillation for distributed edge learning with heterogeneous data,” in Proc. IEEE PIMRC , Istanbul, Turkey, Sep. 2019, pp. 1–6
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Itahara, T. Nishio, M. Morikura, and K. Yamamoto, “A study for knowlege distillation based semi-supervised federated learning with low communication cost,” in Proc. RISING , Tokyo, Japan, Nov. 2019, pp. 1–1
2019
Cited alongside, same era.
T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge,” in Proc. IEEE ICC , Shanghai, China, May 2019, pp. 1–7
2019
Cited alongside, same era.
S. Itahara, T. Nishio, M. Morikura, and K. Yamamoto, “Lottery hypothesis based unsupervised pre-training for model compression in federated learning,” in VTC-fall , held online, Nov. 2020, pp. 1–5
2020
Closest in time.
F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-i.i.d. data,” IEEE Trans. Neural Netw. Learn. Syst , vol. 31, no. 9, pp. 3400–3413, Sep. 2020
2020
Closest in time.
Z. Xianglong, F. Anmin, W. Huaqun, Z. Chunyi, and C. Zhenzhu, “A privacy-preserving and verifiable federated learning scheme,” in Proc. IEEE ICC , held online, Jun. 2020, pp. 1–6
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” in Proc. AISTATS , held online, Jun. 2020, pp. 2938–2948
2020
Closest in time.
2020
Closest in time.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings et al. , “Advances and open problems in federated learning,” to be published at Found. Trends Mach. Learn. , vol. 14, no. 1, Mar. 2021
2021
Closest in time.