Built on

T. Alves, “Trustzone: Integrated hardware and software security,” White paper
2004
Earlier work this paper cites.
T. Dierks and E. Rescorla, “The transport layer security (tls) protocol version 1.2,” tech. rep., 2008
2008
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A. Krizhevsky, V. Nair, and G. Hinton, “Learning multiple layers of features from tiny images.,” 2009
2009
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I. S. Haque and V. S. Pande, “Hard data on soft errors: A large-scale assessment of real-world error rates in gpgpu,” in 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
2010
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Y. LeCun and C. Cortes, “MNIST handwritten digit database.,” 2010
2010
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F. McKeen, I. Alexandrovich, A. Berenzon, C. V. Rozas, H. Shafi, V. Shanbhogue, and U. R. Savagaonkar, “Innovative instructions and software model for isolated execution.,” HASP@ISCA
2013
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K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556
Original
2014
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J. Konečnỳ, B. McMahan, and D. Ramage, “Federated optimization: Distributed optimization beyond the datacenter,” arXiv preprint arXiv:1511.03575
Original
2015
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J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492
Original
2016
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V. Costan and S. Devadas, “Intel SGX Explained,” IACR Cryptology ePrint Archive
2016
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B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial Intelligence and Statistics
2017
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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
2017
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Y. Chen, L. Su, and J. Xu, “Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,” Proceedings of the ACM on Measurement and Analysis of Computing Systems
2017
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2018
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L. Su and J. Xu, “Securing distributed machine learning in high dimensions,” arXiv preprint arXiv:1804.10140
Original
2018
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R. Guerraoui, S. Rouault, et al
2018
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C. Fung, C. J. Yoon, and I. Beschastnikh, “Mitigating sybils in federated learning poisoning,” arXiv preprint arXiv:1808.04866
Original
2018
Earlier work this paper cites.

Similar

Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-iid data,” arXiv preprint arXiv:1806.00582
Original
2018
Cited alongside, same era.
T. Hunt, C. Song, R. Shokri, V. Shmatikov, and E. Witchel, “Chiron: Privacy-preserving machine learning as a service,” arXiv preprint arXiv:1803.05961
Original
2018
Cited alongside, same era.
Z. Zhao, K. M. Barijough, and A. Gerstlauer, “Deepthings: Distributed adaptive deep learning inference on resource-constrained iot edge clusters,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
2018
Cited alongside, same era.
S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “When edge meets learning: Adaptive control for resource-constrained distributed machine learning,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications
2018
Cited alongside, same era.

Then

E.-M. El-Mhamdi, R. Guerraoui, and S. Rouault, “Distributed momentum for byzantine-resilient learning,” arXiv preprint arXiv:2003.00010
Original
2020
Closest in time.
L. He, S. P. Karimireddy, and M. Jaggi, “Byzantine-robust learning on heterogeneous datasets via resampling,” arXiv preprint arXiv:2006.09365
Original
2020
Closest in time.
J. Peng, Z. Wu, and Q. Ling, “Byzantine-robust variance-reduced federated learning over distributed non-iid data,” arXiv preprint arXiv:2009.08161
Original
2020
Closest in time.
J. So, B. Güler, and A. S. Avestimehr, “Byzantine-resilient secure federated learning,” IEEE Journal on Selected Areas in Communications
2020
Closest in time.
C. Kockan, K. Zhu, N. Dokmai, N. Karpov, M. O. Kulekci, D. P. Woodruff, and S. C. Sahinalp, “Sketching algorithms for genomic data analysis and querying in a secure enclave,” Nature methods

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Reading the bibliography…

2020

Secure and Fault Tolerant Decentralized Learning

Prakash, Saurav, Hashemi, Hanieh, Wang, Yongqin et al.

Understand

Federated learning (FL) is a promising paradigm for training a global model over data distributed across multiple data owners without centralizing clients' raw data.

However, sharing of local model updates can also reveal information of clients' local datasets.
Trusted execution environments (TEEs) within the FL server have been recently deployed by companies like Meta for secure aggregation.
However, secure aggregation can suffer from error-prone local updates sent by clients that become faulty during training due to underlying device malfunctions.

Built on

T. Alves, “Trustzone: Integrated hardware and software security,” White paper
2004
Earlier work this paper cites.
T. Dierks and E. Rescorla, “The transport layer security (tls) protocol version 1.2,” tech. rep., 2008
2008
Earlier work this paper cites.
A. Krizhevsky, V. Nair, and G. Hinton, “Learning multiple layers of features from tiny images.,” 2009
2009
Earlier work this paper cites.
I. S. Haque and V. S. Pande, “Hard data on soft errors: A large-scale assessment of real-world error rates in gpgpu,” in 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
2010
Earlier work this paper cites.
Y. LeCun and C. Cortes, “MNIST handwritten digit database.,” 2010
2010
Earlier work this paper cites.
F. McKeen, I. Alexandrovich, A. Berenzon, C. V. Rozas, H. Shafi, V. Shanbhogue, and U. R. Savagaonkar, “Innovative instructions and software model for isolated execution.,” HASP@ISCA
2013
Earlier work this paper cites.
S. Turner, “Transport layer security,” IEEE Internet Computing
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556
Original
2014
Earlier work this paper cites.
J. Konečnỳ, B. McMahan, and D. Ramage, “Federated optimization: Distributed optimization beyond the datacenter,” arXiv preprint arXiv:1511.03575
Original
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,” arXiv preprint arXiv:1610.05492
Original
2016
Earlier work this paper cites.
V. Costan and S. Devadas, “Intel SGX Explained,” IACR Cryptology ePrint Archive
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 Artificial Intelligence and Statistics
2017
Earlier work this paper cites.
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
2017
Earlier work this paper cites.
Y. Chen, L. Su, and J. Xu, “Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,” Proceedings of the ACM on Measurement and Analysis of Computing Systems
2017
Earlier work this paper cites.
P. Blanchard, R. Guerraoui, J. Stainer, et al
2017
Earlier work this paper cites.
D. Yin, Y. Chen, K. Ramchandran, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” ICML
2018
Earlier work this paper cites.
L. Su and J. Xu, “Securing distributed machine learning in high dimensions,” arXiv preprint arXiv:1804.10140
Original
2018
Earlier work this paper cites.
R. Guerraoui, S. Rouault, et al
2018
Earlier work this paper cites.
C. Fung, C. J. Yoon, and I. Beschastnikh, “Mitigating sybils in federated learning poisoning,” arXiv preprint arXiv:1808.04866
Original
2018
Earlier work this paper cites.

Similar

Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-iid data,” arXiv preprint arXiv:1806.00582
Original
2018
Cited alongside, same era.
T. Hunt, C. Song, R. Shokri, V. Shmatikov, and E. Witchel, “Chiron: Privacy-preserving machine learning as a service,” arXiv preprint arXiv:1803.05961
Original
2018
Cited alongside, same era.
Z. Zhao, K. M. Barijough, and A. Gerstlauer, “Deepthings: Distributed adaptive deep learning inference on resource-constrained iot edge clusters,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
2018
Cited alongside, same era.
S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “When edge meets learning: Adaptive control for resource-constrained distributed machine learning,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications
2018
Cited alongside, same era.

Then

E.-M. El-Mhamdi, R. Guerraoui, and S. Rouault, “Distributed momentum for byzantine-resilient learning,” arXiv preprint arXiv:2003.00010
Original
2020
Closest in time.
L. He, S. P. Karimireddy, and M. Jaggi, “Byzantine-robust learning on heterogeneous datasets via resampling,” arXiv preprint arXiv:2006.09365
Original
2020
Closest in time.
J. Peng, Z. Wu, and Q. Ling, “Byzantine-robust variance-reduced federated learning over distributed non-iid data,” arXiv preprint arXiv:2009.08161
Original
2020
Closest in time.
J. So, B. Güler, and A. S. Avestimehr, “Byzantine-resilient secure federated learning,” IEEE Journal on Selected Areas in Communications
2020
Closest in time.
C. Kockan, K. Zhu, N. Dokmai, N. Karpov, M. O. Kulekci, D. P. Woodruff, and S. C. Sahinalp, “Sketching algorithms for genomic data analysis and querying in a secure enclave,” Nature methods

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2019

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L. Zhu, Z. Liu, and S. Han, “Deep leakage from gradients,” in Advances in Neural Information Processing Systems

2019

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L. Lamport, R. Shostak, and M. Pease, “The Byzantine generals problem,” in Concurrency: the Works of Leslie Lamport

2019

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C. Xie, S. Koyejo, and I. Gupta, “Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance,” in International Conference on Machine Learning

2019

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K. Pillutla, S. M. Kakade, and Z. Harchaoui, “Robust aggregation for federated learning,” arXiv preprint arXiv:1912.13445

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2019

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L. Li, W. Xu, T. Chen, G. B. Giannakis, and Q. Ling, “RSA: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets,” in Proceedings of the AAAI Conference on Artificial Intelligence

2019

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F. Tramer and D. Boneh, “Slalom: Fast, verifiable and private execution of neural networks in trusted hardware,” International Conference on Learning Representations (ICLR)

2019

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T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine

2020

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2020

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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al

2021

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P. Kairouz, H. B. McMahan, et al

2021

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X. Cao, M. Fang, J. Liu, and N. Z. Gong, “FLTrust: Byzantine-robust federated learning via trust bootstrapping,” Network and Distributed Systems Security (NDSS) Symposium

2021

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F. Mo, H. Haddadi, K. Katevas, E. Marin, D. Perino, and N. Kourtellis, “PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments,” in Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

2021

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N. Dokmai, C. Kockan, K. Zhu, X. Wang, S. C. Sahinalp, and H. Cho, “Privacy-preserving genotype imputation in a trusted execution environment,” Cell Systems

2021

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H. Hashemi, Y. Wang, and M. Annavaram, “DarKnight: An accelerated framework for privacy and integrity preserving deep learning using trusted hardware,” in MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture

2021

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A. R. Elkordy and A. S. Avestimehr, “HeteroSAg: Secure aggregation with heterogeneous quantization in federated learning,” IEEE Transactions on Communications

2022

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J. Salter, “Containerize all the things! Arm v9 takes security seriously.” https://blog.openmined.org/pysyft-pytorch-intel-sgx/ · 2022

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J. Nguyen, K. Malik, H. Zhan, A. Yousefpour, M. Rabbat, M. Malek, and D. Huba, “Federated learning with buffered asynchronous aggregation,” in International Conference on Artificial Intelligence and Statistics (AISTATS)

2022

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H. Emanuel, “PySyft, PyTorch and Intel SGX: Secure aggregation on trusted execution environments.” https://blog.openmined.org/pysyft-pytorch-intel-sgx/ · 2022

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A. Dhasade, N. Dresevic, A.-M. Kermarrec, and R. Pires, “Tee-based decentralized recommender systems: The raw data sharing redemption,” in IEEE International Parallel and Distributed Processing Symposium (IPDPS)

2022

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Intel, “What Technology Change Enables 1 Terabyte (TB) Enclave Page Cache (EPC) size in 3rd Generation Intel® Xeon® Scalable Processor Platforms?.” https://www.intel.com/content/www/us/en/support/articles/000059614/software/intel-security-products.html · 2022

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Y. Niu, R. E. Ali, and S. Avestimehr, “3LegRace: Privacy-Preserving DNN Training over TEEs and GPUs,” in Proceedings on Privacy Enhancing Technologies (PoPETs)

Original

2022

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