Fetching the paper…
Reading the bibliography…
Even though recent years have seen many attacks exposing severe vulnerabilities in Federated Learning (FL), a holistic understanding of what enables these attacks and how they can be mitigated effectively is still lacking.
J. M. Pollard, “Monte carlo methods for index computation (mod p),” Mathematics of Computation
1978
Earlier work this paper cites.
T. ElGamal, “A public key cryptosystem and a signature scheme based on discrete logarithms,” in Advances in Cryptology
1985
Earlier work this paper cites.
A. Fiat and A. Shamir, “How to Prove Yourself: Practical Solutions to Identification and Signature Problems,” in CRYPTO
1987
Earlier work this paper cites.
M. Blum, P. Feldman, and S. Micali, “Non-interactive zero-knowledge and its applications,” in ACM STOC
1988
Earlier work this paper cites.
T. P. Pedersen, “Non-Interactive and Information-Theoretic secure verifiable secret sharing,” in CRYPTO
1992
Earlier work this paper cites.
J. Camenisch and M. Stadler, “Proof systems for general statements about discrete logarithms,” Technical report/Dept. of Computer Science, ETH Zürich
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput
1997
Earlier work this paper cites.
J. Canny, “Collaborative Filtering with Privacy,” in IEEE Symposium on Security and Privacy (SP)
2002
Earlier work this paper cites.
C. Castelluccia, A. C.-F. Chan, E. Mykletun, and G. Tsudik, “Efficient and provably secure aggregation of encrypted data in wireless sensor networks,” ACM Trans. Sen. Netw
2009
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al
2009
Earlier work this paper cites.
Y. Duan, N. Youdao, J. F. Canny, and J. Z. Zhan, “P4P: Practical Large-Scale Privacy-Preserving distributed computation robust against malicious users,” in USENIX Security
2010
Earlier work this paper cites.
G. Ács and C. Castelluccia, “I have a DREAM! (DiffeRentially private smart metering),” in Information Hiding
2011
Earlier work this paper cites.
K. Kursawe, G. Danezis, and M. Kohlweiss, “Privacy-Friendly aggregation for the Smart-Grid,” in Privacy Enhancing Technologies
2011
Earlier work this paper cites.
R. A. Popa, A. J. Blumberg, H. Balakrishnan, and F. H. Li, “Privacy and accountability for location-based aggregate statistics,” in ACM CCS
2011
Earlier work this paper cites.
E. Shi, H. T. H. Chan, E. Rieffel, R. Chow, and D. Song, “Privacy-preserving aggregation of time-series data,” in NDSS
2011
Earlier work this paper cites.
B. Biggio, B. Nelson, and P. Laskov, “Poisoning Attacks against Support Vector Machines,” in ICML
2012
Earlier work this paper cites.
Z. Brakerski, C. Gentry, and V. Vaikuntanathan, “(leveled) fully homomorphic encryption without bootstrapping,” ACM Trans. Comput. Theory
2014
Earlier work this paper cites.
X. Zhu, D. Anguelov, and D. Ramanan, “Capturing long-tail distributions of object subcategories,” in IEEE CVPR
2014
Earlier work this paper cites.
M. A. et al., “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: http://tensorflow.org/
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Y. LeCun et al
2015
Earlier work this paper cites.
J. Groth, “On the size of Pairing-Based non-interactive arguments,” in Advances in Cryptology – EUROCRYPT 2016
2016
Earlier work this paper cites.
J. Konečný, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon, “Federated Learning: Strategies for Improving Communication Efficiency,” in NeurIPS Workshop on Private Multi-Party Machine Learning
2016
Earlier work this paper cites.
L. Melis, G. Danezis, and E. De Cristofaro, “Efficient private statistics with succinct sketches,” in NDSS
2016
Earlier work this paper cites.
S. Shen, S. Tople, and P. Saxena, “Auror: Defending against poisoning attacks in collaborative deep learning systems,” in ACM ACSAC
2016
Earlier work this paper cites.
P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, “Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent,” in NeurIPS
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 ACM CCS
2017
Earlier work this paper cites.
D. R. Brendan McMahan, “Federated Learning: Collaborative Machine Learning without Centralized Training Data,” Online: https://ai.googleblog.com/2017/04/federated-learning-collaborative.html , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
H. Corrigan-Gibbs and D. Boneh, “Prio: private, robust, and scalable computation of aggregate statistics,” in USENIX NSDI
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas, “Communication-Efficient learning of deep networks from decentralized data,” in AISTATS
2017
Cited alongside, same era.
H. Shafagh, A. Hithnawi, L. Burkhalter, P. Fischli, and S. Duquennoy, “Secure sharing of partially homomorphic encrypted IoT data,” in ACM SenSys
2017
Cited alongside, same era.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in IEEE Symposium on Security and Privacy (SP)
2017
Cited alongside, same era.
J. Böhler and F. Kerschbaum, “Secure Multi-party Computation of Differentially Private Median,” in 29th USENIX Security Symposium (USENIX Security 20)
2020
Later among the works it cites.
D. Cryptography, “Rust Bulletproofs Library,” Online: https://github.com/dalek-cryptography/bulletproofs , August 2020
2020
Later among the works it cites.
——, “Rust Curve25519 Library,” Online: https://github.com/dalek-cryptography/curve25519-dalek , August 2020
2020
Later among the works it cites.
M. Fang, X. Cao, J. Jia, and N. Gong, “Local model poisoning attacks to Byzantine-robust federated learning,” in USENIX Security
2020
Later among the works it cites.
V. Feldman, “Does learning require memorization? a short tale about a long tail,” in ACM STOC
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” in ICLR
2017
Cited alongside, same era.
T. S. Brisimi, R. Chen, T. Mela, A. Olshevsky, I. C. Paschalidis, and W. Shi, “Federated learning of predictive models from federated electronic health records,” Int. J. Med. Inform
2018
Cited alongside, same era.
B. Bünz, J. Bootle, D. Boneh, A. Poelstra, P. Wuille, and G. Maxwell, “Bulletproofs: Short proofs for confidential transactions and more,” in IEEE Symposium on Security and Privacy (SP)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
L. Chen, H. Wang, Z. B. Charles, and D. S. Papailiopoulos, “DRACO: byzantine-resilient distributed training via redundant gradients,” in ICML
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Ji, X. Zhang, S. Ji, X. Luo, and T. Wang, “Model-reuse attacks on deep learning systems,” in ACM CCS
2018
Cited alongside, same era.
V. Feldman and C. Zhang, “What neural networks memorize and why: Discovering the long tail via influence estimation,” in NeurIPS
2020
Later among the works it cites.
H. García Navarro, “Design and implementation of the circom 1.0 compiler,” Master’s thesis, Universidad Complutense de Madrid, 2020. [Online]. Available: https://eprints.ucm.es/id/eprint/62475/
2020
Later among the works it cites.
J. Geiping, H. Bauermeister, H. Droge, and M. Moeller, “Inverting Gradients - How Easy is It to Break Privacy in Federated Learning?” in NeurIPS
2020
Later among the works it cites.
Lukas Burkhalter and Anwar Hithnawi and Alexander Viand and Hossein Shafagh and Sylvia Ratnasamy, “TimeCrypt: Encrypted Data Stream Processing at Scale with Cryptographic Access Control,” in USENIX NSDI
2020
Later among the works it cites.
X. Pan, M. Zhang, D. Wu, Q. Xiao, S. Ji, and Z. Yang, “Justinian’s GAAvernor: Robust Distributed Learning with Gradient Aggregation Agent,” in USENIX Security
2020
Later among the works it cites.
N. Rieke, J. Hancox, W. Li, F. Milletarì, H. R. Roth, S. Albarqouni, S. Bakas, M. N. Galtier, B. A. Landman, K. Maier-Hein, S. Ourselin, M. Sheller, R. M. Summers, A. Trask, D. Xu, M. Baust, and M. J. Cardoso, “The future of digital health with federated learning,” NPJ Digit Med
2020
Later among the works it cites.
M. J. Sheller, B. Edwards, G. A. Reina, J. Martin, S. Pati, A. Kotrotsou, M. Milchenko, W. Xu, D. Marcus, R. R. Colen, and S. Bakas, “Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data,” Sci. Rep
2020
Later among the works it cites.
S. Truex, L. Liu, K.-H. Chow, M. E. Gursoy, and W. Wei, “LDP-Fed: Federated learning with local differential privacy,” in Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking
2020
Later among the works it cites.
H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J.-y. Sohn, K. Lee, and D. Papailiopoulos, “Attack of the Tails: Yes, You Really Can Backdoor Federated Learning,” in NeurIPS
2020
Later among the works it cites.
C. Xie, K. Huang, P.-Y. Chen, and B. Li, “DBA: Distributed Backdoor Attacks against Federated Learning,” in ICLR
2020
Later among the works it cites.
C. Xie, O. Koyejo, and I. Gupta, “Fall of empires: Breaking byzantine-tolerant SGD by inner product manipulation,” in UAI
2020
Later among the works it cites.
C. Zhang, S. Li, X. Junzhe, , W. Wang, F. Yan, and L. Yang, “BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning,” in USENIX ATC
2020
Later among the works it cites.
“Tonic RPC framework,” Online: https://github.com/hyperium/tonic , May 2021
2021
Closest in time.
S. Addanki, K. Garbe, E. Jaffe, R. Ostrovsky, and A. Polychroniadou, “Prio+: Privacy Preserving Aggregate Statistics via Boolean Shares,” Cryptology ePrint Archive cryptoeprint:2021/576
2021
Closest in time.
2021
Closest in time.
D. Boneh, E. Boyle, H. Corrigan-Gibbs, N. Gilboa, and Y. Ishai, “Lightweight Techniques for Private Heavy Hitters,” in IEEE Symposium on Security and Privacy (SP)
2021
Closest in time.
N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson, A. Oprea, and C. Raffel, “Extracting training data from large language models,” in USENIX Security
2021
Closest in time.
2021
Closest in time.
Lukas Burkhalter, Nicolas Kuchler, Alexander Viand, Hossein Shafagh, Anwar Hithnawi, “Zeph: Cryptographic Enforcement of End-to-End Data Privacy,” in USENIX OSDI
2021
Closest in time.
2021
Closest in time.
H. Yin, A. Mallya, A. Vahdat, J. M. Alvarez, J. Kautz, and P. Molchanov, “See through gradients: Image batch recovery via gradinversion,” in CVPR
2021
Closest in time.
M. Naseri, J. Hayes, and E. De Cristofaro, “Local and central differential privacy for robustness and privacy in federated learning,” in NDSS
2022
Closest in time.
V. Shejwalkar, A. Houmansadr, P. Kairouz, and D. Ramage, “Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning,” in IEEE Symposium on Security and Privacy (SP)
2022
Closest in time.
2022
Closest in time.
Z. Zhang, A. Panda, L. Song, Y. Yang, M. Mahoney, P. Mittal, R. Kannan, and J. Gonzalez, “Neurotoxin: Durable backdoors in federated learning,” in ICML
2022
Closest in time.