Defense against local model poisoning attacks to byzantine-robust federated learning
Shiwei LU, Ruihu LI, Xuan CHEN, Yuena MA
Defense against local model poisoning attacks to byzantine-robust federated learning
[1] |
McMahan B, Moore E, Ramage D, Hampson S, Arcas B A. Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR. 2017, 1273−1282
|
[2] |
Kairouz P, McMahan B, Avent B, Bellet A, Bennis M, Bhagoji A N, Bonawitz K. Advances and open problems in federated learning. 2019, arXiv preprint arXiv: 2019.04977
|
[3] |
Blanchard P, El Mhamdi E M, Guerraoui R, Stainer J. Machine learning with adversaries: Byzantine tolerant gradient descent. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 118–128
|
[4] |
Yin D, Chen Y D, Kannan R, Bartlett P. Byzantine-robust distributed learning: towards optimal statistical rates. In: Proceedings of the 35th International Conference on Machine Learning. 2018, 2018−5659
|
[5] |
Fang M H, Cao X Y, Jia J Y, Gong Z Q. Local model poisoning attacks to Byzantine-robust federated learning. In: Proceedings of the 29th Usenix Security Symposium. 2020
|
[6] |
Li S, Cheng Y, Wang W, Liu Y, Chen T J. Learning to detect malicious clients for robust federated learning. 2020, arXiv preprint arXiv: 2020.00211
|
[7] |
Zong B, Song Q, Min M R, Cheng W, Lumezanu C, Cho D, Chen H F. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: Proceedings of the 27th International Conference on Learning Representations. 2018
|
/
〈 | 〉 |