Built on

C. Fung, C. J. M. Yoon, and I. Beschastnikh, “Mitigating sybils in federated learning poisoning,” CoRR , vol. abs/1808.04866, 2018. [Online]. Available: http://arxiv.org/abs/1808.04866
Original
1911
Earlier work this paper cites.
Y. Le Cun, “Learning process in an asymmetric threshold network,” in Disordered Systems and Biological Organization , E. Bienenstock, F. F. Soulié, and G. Weisbuch, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986, pp. 233–240
1986
Earlier work this paper cites.
H. Bourlard and Y. Kamp, “Auto-association by multilayer perceptrons and singular value decomposition,” Biological Cybernetics , vol. 59, no. 4, pp. 291–294, Sep 1988. [Online]. Available: https://doi.org/10.1007/BF00332918
1988
Earlier work this paper cites.
W. J. Baumol, Welfare Economics and the Theory of the State . Boston, MA: Springer US, 2004, pp. 937–940
2004
Earlier work this paper cites.
M. Feldman and J. Chuang, “Overcoming free-riding behavior in peer-to-peer systems,” SIGecom Exch. , vol. 5, no. 4, pp. 41–50, Jul. 2005. [Online]. Available: http://doi.acm.org/10.1145/1120717.1120723
2005
Earlier work this paper cites.
T. Locher, P. Moore, S. Schmid, and R. Wattenhofer, “Free riding in bittorrent is cheap,” in 5th Workshop on Hot Topics in Networks (HotNets) , 2006. [Online]. Available: http://eprints.cs.univie.ac.at/5696/
2006
Earlier work this paper cites.
M. Feldman, C. Papadimitriou, J. Chuang, and I. Stoica, “Free-riding and whitewashing in peer-to-peer systems,” IEEE Journal on Selected Areas in Communications , vol. 24, no. 5, pp. 1010–1019, May 2006
2006
Earlier work this paper cites.
C. Dwork, “Differential privacy,” in 33rd International Colloquium on Automata, Languages and Programming, part II (ICALP 2006) , ser. Lecture Notes in Computer Science, vol. 4052. Springer Verlag, July 2006, pp. 1–12. [Online]. Available: https://www.microsoft.com/en-us/research/publication/differential-privacy/
2006
Earlier work this paper cites.
L. Deng, “The mnist database of handwritten digit images for machine learning research [best of the web],” IEEE Signal Processing Magazine , vol. 29, no. 6, pp. 141–142, 2012
2012
Earlier work this paper cites.
M. Sakurada and T. Yairi, “Anomaly detection using autoencoders with nonlinear dimensionality reduction,” in Proceedings of the MLSDA 2014 2Nd Workshop on Machine Learning for Sensory Data Analysis , ser. MLSDA’14. New York, NY, USA: ACM, 2014, pp. 4:4–4:11. [Online]. Available: http://doi.acm.org/10.1145/2689746.2689747
2014
Earlier work this paper cites.
R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton) , Sep. 2015, pp. 909–910
2015
Earlier work this paper cites.
H. B. McMahan, E. Moore, D. Ramage, and B. A. y Arcas, “Federated learning of deep networks using model averaging,” CoRR , vol. abs/1602.05629, 2016. [Online]. Available: http://arxiv.org/abs/1602.05629
Original
2016
Earlier work this paper cites.

Similar

Then

T. Brisimi, R. Chen, T. Mela, A. Olshevsky, I. Paschalidis, and W. Shi, “Federated learning of predictive models from federated electronic health records,” International Journal of Medical Informatics , vol. 112, 01 2018
2018
Later among the works it cites.
J.-S. Weng, J. Weng, M. Li, Y. Zhang, and W. Luo, “Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive,” IACR Cryptology ePrint Archive , vol. 2018, p. 679, 2018
2018
Later among the works it cites.
B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen, “Deep autoencoding gaussian mixture model for unsupervised anomaly detection,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=BJJLHbb0-
2018
Later among the works it cites.
A. Borghesi, A. Bartolini, M. Lombardi, M. Milano, and L. Benini, “Anomaly detection using autoencoders in high performance computing systems,” CoRR , vol. abs/1811.05269, 2018. [Online]. Available: http://arxiv.org/abs/1811.05269
Original

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2019

Free-riders in Federated Learning: Attacks and Defenses

Lin, Jierui, Du, Min, Liu, Jian

Understand

Federated learning is a recently proposed paradigm that enables multiple clients to collaboratively train a joint model.

It allows clients to train models locally, and leverages the parameter server to generate a global model by aggregating the locally submitted gradient updates at each round.
Although the incentive model for federated learning has not been fully developed, it is supposed that participants are able to get rewards or the privilege to use the final global model, as a compensation for taking efforts to train the model.
Therefore, a client who does not have any local data has the incentive to construct local gradient updates in order to deceive for rewards.

Built on

C. Fung, C. J. M. Yoon, and I. Beschastnikh, “Mitigating sybils in federated learning poisoning,” CoRR , vol. abs/1808.04866, 2018. [Online]. Available: http://arxiv.org/abs/1808.04866
Original
1911
Earlier work this paper cites.
Y. Le Cun, “Learning process in an asymmetric threshold network,” in Disordered Systems and Biological Organization , E. Bienenstock, F. F. Soulié, and G. Weisbuch, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986, pp. 233–240
1986
Earlier work this paper cites.
H. Bourlard and Y. Kamp, “Auto-association by multilayer perceptrons and singular value decomposition,” Biological Cybernetics , vol. 59, no. 4, pp. 291–294, Sep 1988. [Online]. Available: https://doi.org/10.1007/BF00332918
1988
Earlier work this paper cites.
W. J. Baumol, Welfare Economics and the Theory of the State . Boston, MA: Springer US, 2004, pp. 937–940
2004
Earlier work this paper cites.
M. Feldman and J. Chuang, “Overcoming free-riding behavior in peer-to-peer systems,” SIGecom Exch. , vol. 5, no. 4, pp. 41–50, Jul. 2005. [Online]. Available: http://doi.acm.org/10.1145/1120717.1120723
2005
Earlier work this paper cites.
T. Locher, P. Moore, S. Schmid, and R. Wattenhofer, “Free riding in bittorrent is cheap,” in 5th Workshop on Hot Topics in Networks (HotNets) , 2006. [Online]. Available: http://eprints.cs.univie.ac.at/5696/
2006
Earlier work this paper cites.
M. Feldman, C. Papadimitriou, J. Chuang, and I. Stoica, “Free-riding and whitewashing in peer-to-peer systems,” IEEE Journal on Selected Areas in Communications , vol. 24, no. 5, pp. 1010–1019, May 2006
2006
Earlier work this paper cites.
C. Dwork, “Differential privacy,” in 33rd International Colloquium on Automata, Languages and Programming, part II (ICALP 2006) , ser. Lecture Notes in Computer Science, vol. 4052. Springer Verlag, July 2006, pp. 1–12. [Online]. Available: https://www.microsoft.com/en-us/research/publication/differential-privacy/
2006
Earlier work this paper cites.
L. Deng, “The mnist database of handwritten digit images for machine learning research [best of the web],” IEEE Signal Processing Magazine , vol. 29, no. 6, pp. 141–142, 2012
2012
Earlier work this paper cites.
M. Sakurada and T. Yairi, “Anomaly detection using autoencoders with nonlinear dimensionality reduction,” in Proceedings of the MLSDA 2014 2Nd Workshop on Machine Learning for Sensory Data Analysis , ser. MLSDA’14. New York, NY, USA: ACM, 2014, pp. 4:4–4:11. [Online]. Available: http://doi.acm.org/10.1145/2689746.2689747
2014
Earlier work this paper cites.
R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton) , Sep. 2015, pp. 909–910
2015
Earlier work this paper cites.
H. B. McMahan, E. Moore, D. Ramage, and B. A. y Arcas, “Federated learning of deep networks using model averaging,” CoRR , vol. abs/1602.05629, 2016. [Online]. Available: http://arxiv.org/abs/1602.05629
Original
2016
Earlier work this paper cites.

Similar

Then

T. Brisimi, R. Chen, T. Mela, A. Olshevsky, I. Paschalidis, and W. Shi, “Federated learning of predictive models from federated electronic health records,” International Journal of Medical Informatics , vol. 112, 01 2018
2018
Later among the works it cites.
J.-S. Weng, J. Weng, M. Li, Y. Zhang, and W. Luo, “Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive,” IACR Cryptology ePrint Archive , vol. 2018, p. 679, 2018
2018
Later among the works it cites.
B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen, “Deep autoencoding gaussian mixture model for unsupervised anomaly detection,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=BJJLHbb0-
2018
Later among the works it cites.
A. Borghesi, A. Bartolini, M. Lombardi, M. Milano, and L. Benini, “Anomaly detection using autoencoders in high performance computing systems,” CoRR , vol. abs/1811.05269, 2018. [Online]. Available: http://arxiv.org/abs/1811.05269
Original

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

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,” CoRR , vol. abs/1610.05492, 2016. [Online]. Available: http://arxiv.org/abs/1610.05492

Original

2016

Cited alongside, same era.

S. Shen, S. Tople, and P. Saxena, “Auror: Defending against poisoning attacks in collaborative deep learning systems,” in Proceedings of the 32Nd Annual Conference on Computer Security Applications , ser. ACSAC ’16. New York, NY, USA: ACM, 2016, pp. 508–519. [Online]. Available: http://doi.acm.org/10.1145/2991079.2991125

2016

Cited alongside, same era.

R. C. Geyer, T. Klein, and M. Nabi, “Differentially private federated learning: A client level perspective,” CoRR , vol. abs/1712.07557, 2017. [Online]. Available: http://arxiv.org/abs/1712.07557

Original

2017

Cited alongside, same era.

K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’17. New York, NY, USA: ACM, 2017, pp. 1175–1191. [Online]. Available: http://doi.acm.org/10.1145/3133956.3133982

2017

Cited alongside, same era.

B. Hitaj, G. Ateniese, and F. Pérez-Cruz, “Deep models under the GAN: information leakage from collaborative deep learning,” CoRR , vol. abs/1702.07464, 2017. [Online]. Available: http://arxiv.org/abs/1702.07464

Original

2017

Cited alongside, same era.

H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang, “Learning differentially private language models without losing accuracy,” CoRR , vol. abs/1710.06963, 2017. [Online]. Available: http://arxiv.org/abs/1710.06963

Original

2017

Cited alongside, same era.

A. Hard, K. Rao, R. Mathews, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage, “Federated learning for mobile keyboard prediction,” CoRR , vol. abs/1811.03604, 2018. [Online]. Available: http://arxiv.org/abs/1811.03604

Original