Author:
Asare Bernard Atiemo,Branco Paula,Kiringa Iluju,Yeap Tet
Publisher
Springer Nature Switzerland
Reference21 articles.
1. Khojir, H.F., et al.: FedShare: secure aggregation based on additive secret sharing in federated learning. In: International Database Engineered Applications Symposium Conference, Heraklion, Crete Greece, 2023, pp. 25–33. ACM (2023). https://dl.acm.org/doi/10.1145/3589462.3589504
2. Bell, J.H., et al.: Secure single-server aggregation with (poly)logarithmic overhead. In: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, pp. 1253–1269 (2020)
3. Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175–1191 (2017)
4. Bouacida, N., Mohapatra, P.: Vulnerabilities in federated learning. IEEE Access 9, 63229–63249 (2021). https://doi.org/10.1109/ACCESS.2021.3075203
5. Choudhury, A., Patra, A.: Secret sharing. In: Secure Multi-Party Computation Against Passive Adversaries. Synthesis Lectures on Distributed Computing Theory, pp. 17–31. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-12164-7_3