Built on

Singh, A., Vepakomma, P., Gupta, O., Raskar, R.: Detailed comparison of communication efficiency of split learning and federated learning. arxiv (2019), https://arxiv.org/abs/1909.09145
Original
1909
Earlier work this paper cites.
Kairouz, P., McMahan, H.B., et al.: Advances and open problems in federated learning. CoRR abs/1912.04977
Original
1912
Earlier work this paper cites.
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proc. of the IEEE. vol. 86, pp. 2278–2324 (Nov 1998). https://doi.org/10.1109/5.726791
1998
Earlier work this paper cites.
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. of the IEEE 86
1998
Earlier work this paper cites.
Gao, Y., Kim, M., Abuadbba, S., Kim, Y., Thapa, C., Kim, K., Camtepe, S.A., Kim, H., Nepal, S.: End-to-end evaluation of federated learning and split learning for internet of things. In: Proc. SRDS (2020), https://arxiv.org/pdf/2003.13376.pdf
Original
2003
Earlier work this paper cites.
Gentry, C.: A fully homomorphic encryption scheme. Ph.D. thesis, Stanford University, Stanford, California (Sept 2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proc. NIPS’12 - Vol. 1. pp. 1097–1105. USA (2012)
2012
Earlier work this paper cites.
Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science 9
2014
Earlier work this paper cites.

Similar

Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science 9
2014
Cited alongside, same era.
Konecný, J., McMahan, B., Ramage, D.: Federated optimization: Distributed optimization beyond the datacenter. arxiv (2015), https://arxiv.org/pdf/1511.03575.pdf
Original
2015
Cited alongside, same era.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proc. 3rd ICLR (2015), http://arxiv.org/abs/1409.1556
Original
2015
Cited alongside, same era.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. pp. 308–318 (2016)
2016
Cited alongside, same era.

Then

Gupta, O., Raskar, R.: Distributed learning of deep neural network over multiple agents. J. Network and Computer Applications 116
2018
Later among the works it cites.
Tschandl, P.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions (2018), doi:10.7910/DVN/DBW86T
2018
Later among the works it cites.
Vepakomma, P., Gupta, O., Swedish, T., Raskar, R.: Split learning for health: Distributed deep learning without sharing raw patient data. arxiv (2018), http://arxiv.org/abs/1812.00564
Original
2018
Later among the works it cites.
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konecný, J., Mazzocchi, S., McMahan, H.B., Overveldt, T.V., Petrou, D., Ramage, D., Roselander, J.: Towards federated learning at scale: System design. In: Proc. SysML Conference. pp. 1–15 (2019), https://mlsys.org/Conferences/2019/doc/2019/193.pdf
2019
Later among the works it cites.

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2020

SplitFed: When Federated Learning Meets Split Learning

Thapa, Chandra, Chamikara, M. A. P., Camtepe, Seyit et al.

Understand

Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches.

Both follow a model-to-data scenario; clients train and test machine learning models without sharing raw data.
SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server.
Moreover, the split model makes SL a better option for resource-constrained environments.

Built on

Singh, A., Vepakomma, P., Gupta, O., Raskar, R.: Detailed comparison of communication efficiency of split learning and federated learning. arxiv (2019), https://arxiv.org/abs/1909.09145
Original
1909
Earlier work this paper cites.
Kairouz, P., McMahan, H.B., et al.: Advances and open problems in federated learning. CoRR abs/1912.04977
Original
1912
Earlier work this paper cites.
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proc. of the IEEE. vol. 86, pp. 2278–2324 (Nov 1998). https://doi.org/10.1109/5.726791
1998
Earlier work this paper cites.
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. of the IEEE 86
1998
Earlier work this paper cites.
Gao, Y., Kim, M., Abuadbba, S., Kim, Y., Thapa, C., Kim, K., Camtepe, S.A., Kim, H., Nepal, S.: End-to-end evaluation of federated learning and split learning for internet of things. In: Proc. SRDS (2020), https://arxiv.org/pdf/2003.13376.pdf
Original
2003
Earlier work this paper cites.
Gentry, C.: A fully homomorphic encryption scheme. Ph.D. thesis, Stanford University, Stanford, California (Sept 2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proc. NIPS’12 - Vol. 1. pp. 1097–1105. USA (2012)
2012
Earlier work this paper cites.
Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science 9
2014
Earlier work this paper cites.

Similar

Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science 9
2014
Cited alongside, same era.
Konecný, J., McMahan, B., Ramage, D.: Federated optimization: Distributed optimization beyond the datacenter. arxiv (2015), https://arxiv.org/pdf/1511.03575.pdf
Original
2015
Cited alongside, same era.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proc. 3rd ICLR (2015), http://arxiv.org/abs/1409.1556
Original
2015
Cited alongside, same era.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. pp. 308–318 (2016)
2016
Cited alongside, same era.

Then

Gupta, O., Raskar, R.: Distributed learning of deep neural network over multiple agents. J. Network and Computer Applications 116
2018
Later among the works it cites.
Tschandl, P.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions (2018), doi:10.7910/DVN/DBW86T
2018
Later among the works it cites.
Vepakomma, P., Gupta, O., Swedish, T., Raskar, R.: Split learning for health: Distributed deep learning without sharing raw patient data. arxiv (2018), http://arxiv.org/abs/1812.00564
Original
2018
Later among the works it cites.
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konecný, J., Mazzocchi, S., McMahan, H.B., Overveldt, T.V., Petrou, D., Ramage, D., Roselander, J.: Towards federated learning at scale: System design. In: Proc. SysML Conference. pp. 1–15 (2019), https://mlsys.org/Conferences/2019/doc/2019/193.pdf
2019
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Dwork, C., McSherry, F., Nissim, K., Smith, A.D.: Calibrating noise to sensitivity in private data analysis. J. Priv. Confidentiality 7

2016

Cited alongside, same era.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proc. IEEE CVPR. pp. 770–778 (June 2016). https://doi.org/10.1109/CVPR.2016.90

2016

Cited alongside, same era.

McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Proc. AISTATS. pp. 1273–1282 (2017)

2017

Cited alongside, same era.

Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arxiv (2017), http://arxiv.org/abs/1708.07747

Original

2017

Cited alongside, same era.

Krizhevsky, A., Nair, V., Hinton, G.: Cifar-10 (canadian institute for advanced research) Http://www.cs.toronto.edu/ kriz/cifar.html

Cited in the paper.

Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., Jana, S.: Certified robustness to adversarial examples with differential privacy. In: 2019 IEEE Symposium on Security and Privacy (SP). pp. 656–672. IEEE (2019)

2019

Later among the works it cites.

Vepakomma, P., Gupta, O., Dubey, A., Raskar, R.: Reducing leakage in distributed deep learning for sensitive health data. In: Proc. ICLR (2019)

2019

Later among the works it cites.

Gao, Y., Kim, M., Thapa, C., Abuadbba, S., Zhang, Z., Camtepe, S., Kim, H., Nepal, S.: Evaluation and optimization of distributed machine learning techniques for internet of things. CoRR abs/2103.02762

Original

2021

Closest in time.

Han, D.J., amd Jungmoon Lee, H.I.B., Moon, J.: Accelerating federated learning with split learning on locally generated losses. In: Proc. FL-ICML (2021)

2021

Closest in time.