Fetching the paper…
Reading the bibliography…
Federated Learning (FL) is a collaborative machine learning paradigm for training models on local sensitive data with privacy protection.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska et al. , “Overcoming catastrophic forgetting in neural networks,” Proceedings of the national academy of sciences , vol. 114, no. 13, pp. 3521–3526, 2017
2017
Earlier work this paper cites.
Z. Li and D. Hoiem, “Learning without forgetting,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 12, pp. 2935–2947, 2017
2017
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 . PMLR, 2017, pp. 1273–1282
2017
Earlier work this paper cites.
R. Aljundi, F. Babiloni, M. Elhoseiny, M. Rohrbach, and T. Tuytelaars, “Memory aware synapses: Learning what (not) to forget,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 139–154
2018
Earlier work this paper cites.
P. Kairouz, H. B. McMahan et al. , “Advances and open problems in federated learning,” Foundations and Trends in Machine Learning , vol. 14, no. 1-2, pp. 1–210, 2021
2021
Earlier work this paper cites.
S. Guo, T. Zhang, H. Yu, X. Xie, L. Ma, T. Xiang, and Y. Liu, “Byzantine-resilient decentralized stochastic gradient descent,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 6, pp. 4096–4106, 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
L. Wang, M. Zhang, Z. Jia, Q. Li, C. Bao, K. Ma, J. Zhu, and Y. Zhong, “Afec: Active forgetting of negative transfer in continual learning,” Advances in Neural Information Processing Systems , vol. 34, pp. 22 379–22 391, 2021
2021
Earlier work this paper cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
L. Qu, Y. Zhou, P. P. Liang, Y. Xia, F. Wang, E. Adeli, L. Fei-Fei, and D. Rubin, “Rethinking architecture design for tackling data heterogeneity in federated learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 10 061–10 071
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
Y. Tian, Y. Wan, L. Lyu, D. Yao, H. Jin, and L. Sun, “Fedbert: When federated learning meets pre-training,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 13, no. 4, pp. 1–26, 2022
2022
Earlier work this paper cites.
G. Sun, M. Mendieta, T. Yang, and C. Chen, “Conquering the communication constraints to enable large pre-trained models in federated learning,” arXiv , 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al. , “Lora: Low-rank adaptation of large language models.” ICLR , vol. 1, no. 2, p. 3, 2022
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
R. Tiwari, K. Killamsetty, R. Iyer, and P. Shenoy, “Gcr: Gradient coreset based replay buffer selection for continual learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 99–108
2022
Cited alongside, same era.
Y. Zhang, B. Pfahringer, E. Frank, A. Bifet, N. J. S. Lim, and Y. Jia, “A simple but strong baseline for online continual learning: Repeated augmented rehearsal,” Advances in Neural Information Processing Systems , vol. 35, pp. 14 771–14 783, 2022
2022
Cited alongside, same era.
Q. Zhang, S. Zuo, C. Liang, A. Bukharin, P. He, W. Chen, and T. Zhao, “Platon: Pruning large transformer models with upper confidence bound of weight importance,” in International conference on machine learning . PMLR, 2022, pp. 26 809–26 823
2022
Q. Gao, X. Shan, Y. Zhang, and F. Zhou, “Enhancing knowledge transfer for task incremental learning with data-free subnetwork,” Advances in Neural Information Processing Systems , vol. 36, pp. 68 471–68 484, 2023
2023
Later among the works it cites.
C. Lamers, R. Vidal, N. Belbachir, N. van Stein, T. Bäeck, and P. Giampouras, “Clustering-based domain-incremental learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3384–3392
2023
Later among the works it cites.
B. Wickramasinghe, G. Saha, and K. Roy, “Continual learning: A review of techniques, challenges and future directions,” IEEE Transactions on Artificial Intelligence , vol. 5, no. 6, pp. 2526–2546, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
E. Malan, V. Peluso, A. Calimera, and E. Macii, “Communication-efficient federated learning with gradual layer freezing,” IEEE Embedded Systems Letters , vol. 15, no. 1, pp. 25–28, 2022
2022
Cited alongside, same era.
C. Liu and H. Yu, “Ai-empowered persuasive video generation: A survey,” ACM Computing Surveys , vol. 55, no. 13s, pp. 1–31, 2023
2023
Cited alongside, same era.
T. Gao, X. Liu, Y. Yang, and G. Wang, “Fedmbp: Multi-branch prototype federated learning on heterogeneous data,” in 2023 IEEE International Conference on Image Processing (ICIP) . IEEE, 2023, pp. 2180–2184
2023
Cited alongside, same era.
O. R. A. Almanifi, C.-O. Chow, M.-L. Tham, J. H. Chuah, and J. Kanesan, “Communication and computation efficiency in federated learning: A survey,” Internet of Things , p. 100742, 2023
2023
Cited alongside, same era.
N. Ding, Y. Qin, G. Yang, F. Wei, Z. Yang, Y. Su, S. Hu, Y. Chen, C.-M. Chan, W. Chen et al. , “Parameter-efficient fine-tuning of large-scale pre-trained language models,” Nature Machine Intelligence , vol. 5, no. 3, pp. 220–235, 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
C. Chen, H. Xu, W. Wang, B. Li, B. Li, L. Chen, and G. Zhang, “Gift: Toward accurate and efficient federated learning with gradient-instructed frequency tuning,” IEEE Journal on Selected Areas in Communications , vol. 41, no. 4, pp. 902–914, 2023
2023
Cited alongside, same era.
2023
Later among the works it cites.
X. Tang and H. Yu, “Efficient large-scale personalizable bidding for multi-agent auction-based federated learning,” IEEE Internet of Things Journal , vol. 11, no. 15, pp. 26 518–26 530, 2024
2024
Closest in time.
Y. Yang, X. Liu, T. Gao, X. Xu, P. Zhang, and G. Wang, “Dense contrastive-based federated learning for dense prediction tasks on medical images,” IEEE Journal of Biomedical and Health Informatics , vol. 28, no. 4, pp. 2055–2066, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
A. Gui, J. Ye, and H. Xiao, “G-adapter: Towards structure-aware parameter-efficient transfer learning for graph transformer networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 11, 2024, pp. 12 226–12 234
2024
Closest in time.
C. Xie, D.-A. Huang, W. Chu, D. Xu, C. Xiao, B. Li, and A. Anandkumar, “Perada: Parameter-efficient federated learning personalization with generalization guarantees,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 23 838–23 848
2024
Closest in time.
2024
Closest in time.
R. A. Bafghi, N. Harilal, C. Monteleoni, and M. Raissi, “Parameter efficient fine-tuning of self-supervised vits without catastrophic forgetting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 3679–3684
2024
Closest in time.
L. Wang, X. Zhang, H. Su, and J. Zhu, “A comprehensive survey of continual learning: theory, method and application,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 8, pp. 5362–5383, 2024
2024
Closest in time.
Y. Chen, A. Z. Tan, S. Feng, H. Yu, T. Deng, L. Zhao, and F. Wu, “General federated class-incremental learning with lightweight generative replay,” IEEE Internet of Things Journal , vol. 11, no. 20, pp. 33 927–33 939, 2024
2024
Closest in time.