Robust Federated Learning for Edge Intelligence
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-58923-2_11
Reference82 articles.
1. Abreha, A.G., Liang, X., Liang, Y.C.: Management of communication resources for federated learning over wireless networks: a survey. IEEE Commun. Surv. Tutorials 24(1), 1–1 (2022)
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3. Bai, Y., Chen, L., Abdel-Mottaleb, M., Xu, J.: Automated ensemble for deep learning inference on edge computing platforms. IEEE Internet Things J. 9(6), 4202–4213 (2022). https://doi.org/10.1109/JIOT.2021.3102945
4. Bankovic, Z., Fraga, D., Moya, J.M., Vallejo, J.C., Araujo, Á., Malagón, P., de Goyeneche, J., Villanueva, D., Romero, E., Blesa, J.: Detecting and confining sybil attack in wireless sensor networks based on reputation systems coupled with self-organizing maps. In: Papadopoulos, H., Andreou, A.S., Bramer, M. (eds.) Artificial Intelligence Applications and Innovations - 6th IFIP WG 12.5 International Conference, AIAI 2010, Larnaca, Cyprus, October 6–7, 2010. Proceedings. IFIP Advances in Information and Communication Technology, vol. 339, pp. 311–318. Springer, Berlin (2010). https://doi.org/10.1007/978-3-642-16239-8_41
5. Bao, X., Su, C., Xiong, Y., Huang, W., Hu, Y.: Flchain: a blockchain for auditable federated learning with trust and incentive. In: 5th International Conference on Big Data Computing and Communications, BIGCOM 2019, QingDao, China, August 9–11, 2019. pp. 151–159. IEEE, Piscataway (2019). https://doi.org/10.1109/BIGCOM.2019.00030
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