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We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N \in \mathbb{N}$ distributed users, each of size $L \in \mathbb{N}$, trained on their local data, in a privacy-preserving manner.
A. Shamir, “How to share a secret,” Communications of the ACM
1979
Earlier work this paper cites.
M. Aigner and G. M. Ziegler, “Proofs from the book,” Berlin. Germany
1999
Earlier work this paper cites.
K. S. Kedlaya and C. Umans, “Fast polynomial factorization and modular composition,” SIAM Journal on Computing
2011
Earlier work this paper cites.
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.
2019
Earlier work this paper cites.
Q. Yu, S. Li, N. Raviv, S. M. M. Kalan, M. Soltanolkotabi, and S. A. Avestimehr, “Lagrange coded computing: Optimal design for resiliency, security, and privacy,” in The 22nd International Conference on Artificial Intelligence and Statistics
2019
Cited alongside, same era.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine
2020
Cited alongside, same era.
L. Zhu and S. Han, “Deep leakage from gradients,” in Federated learning
2020
Cited alongside, same era.
2020
Cited alongside, same era.
J. H. Bell, K. A. Bonawitz, A. Gascón, T. Lepoint, and M. Raykova, “Secure single-server aggregation with (poly) logarithmic overhead,” in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security
2020
Later among the works it cites.
J. So, B. Güler, and A. S. Avestimehr, “Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning,” IEEE Journal on Selected Areas in Information Theory
2021
Later among the works it cites.
Y. Zhao and H. Sun, “Information theoretic secure aggregation with user dropouts,” in 2021 IEEE International Symposium on Information Theory (ISIT)
2021
Later among the works it cites.
R. Schlegel, S. Kumar, E. Rosnes, et al
2021
Later among the works it cites.
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
2022
Closest in time.
T. Jahani-Nezhad, M. A. Maddah-Ali, S. Li, and G. Caire, “Swiftagg: Communication-efficient and dropout-resistant secure aggregation for federated learning with worst-case security guarantees,” in 2022 IEEE International Symposium on Information Theory (ISIT)
2022
Closest in time.