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Federated learning (FL) is a privacy-preserving paradigm for collaboratively training a global model from decentralized clients.
Y. Niu and W. Deng, “Federated learning for face recognition with gradient correction,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 2, 2022, pp. 1999–2007
2007
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M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security , 2016, pp. 308–318
2016
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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 . PMLR, 2017, pp. 1273–1282
2017
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A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
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2018
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2018
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M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4510–4520
2018
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2018
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2019
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2019
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S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” IEEE journal on selected areas in communications , vol. 37, no. 6, pp. 1205–1221, 2019
2019
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2020
Cited alongside, same era.
G. A. Kaissis, M. R. Makowski, D. Rückert, and R. F. Braren, “Secure, privacy-preserving and federated machine learning in medical imaging,” Nature Machine Intelligence , vol. 2, no. 6, pp. 305–311, 2020
2020
Cited alongside, same era.
J. Geiping, H. Bauermeister, H. Dröge, and M. Moeller, “Inverting gradients-how easy is it to break privacy in federated learning?” Advances in Neural Information Processing Systems , vol. 33, pp. 16 937–16 947, 2020
2020
Cited alongside, same era.
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “Scaffold: Stochastic controlled averaging for federated learning,” in International conference on machine learning . PMLR, 2020, pp. 5132–5143
2020
Y. Huang, L. Chu, Z. Zhou, L. Wang, J. Liu, J. Pei, and Y. Zhang, “Personalized cross-silo federated learning on non-iid data,” in Proceedings of the AAAI conference on artificial intelligence , vol. 35, no. 9, 2021, pp. 7865–7873
2021
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L. Mansilla, R. Echeveste, D. H. Milone, and E. Ferrante, “Domain generalization via gradient surgery,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 6630–6638
2021
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M. Jiang, Z. Wang, and Q. Dou, “Harmofl: Harmonizing local and global drifts in federated learning on heterogeneous medical images,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 1, 2022, pp. 1087–1095
2022
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N. Yan, K. Wang, C. Pan, and K. K. Chai, “Performance analysis for channel-weighted federated learning in oma wireless networks,” IEEE Signal Processing Letters , vol. 29, pp. 772–776, 2022
2022
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Cited alongside, same era.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” Proceedings of Machine learning and systems , vol. 2, pp. 429–450, 2020
2020
Cited alongside, same era.
J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor, “Tackling the objective inconsistency problem in heterogeneous federated optimization,” Advances in neural information processing systems , vol. 33, pp. 7611–7623, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
T. Yu, S. Kumar, A. Gupta, S. Levine, K. Hausman, and C. Finn, “Gradient surgery for multi-task learning,” Advances in Neural Information Processing Systems , vol. 33, pp. 5824–5836, 2020
2020
Cited alongside, same era.
2021
Cited alongside, same era.
Q. Li, B. He, and D. Song, “Model-contrastive federated learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 10 713–10 722
2021
Cited alongside, same era.
2022
Later among the works it cites.
Z. Qu, X. Li, R. Duan, Y. Liu, B. Tang, and Z. Lu, “Generalized federated learning via sharpness aware minimization,” in International Conference on Machine Learning . PMLR, 2022, pp. 18 250–18 280
2022
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D. Caldarola, B. Caputo, and M. Ciccone, “Improving generalization in federated learning by seeking flat minima,” in European Conference on Computer Vision . Springer, 2022, pp. 654–672
2022
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X. Ma, J. Zhang, S. Guo, and W. Xu, “Layer-wised model aggregation for personalized federated learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 092–10 101
2022
Later among the works it cites.
M. P. Uddin, Y. Xiang, J. Yearwood, and L. Gao, “Robust federated averaging via outlier pruning,” IEEE Signal Processing Letters , vol. 29, pp. 409–413, 2022
2022
Later among the works it cites.
S. Han, S. Park, F. Wu, S. Kim, C. Wu, X. Xie, and M. Cha, “Fedx: Unsupervised federated learning with cross knowledge distillation,” in European Conference on Computer Vision . Springer, 2022, pp. 691–707
2022
Later among the works it cites.
J. Miao, Z. Yang, L. Fan, and Y. Yang, “Fedseg: Class-heterogeneous federated learning for semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 8042–8052
2023
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