Fetching the paper…
Reading the bibliography…
Federated learning allows multiple clients to collaboratively train a model without exchanging their data, thus preserving data privacy.
J. J. Hull, “A database for handwritten text recognition research,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 16, no. 5, pp. 550–554, May 1994
1994
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE , vol. 86, no. 11, pp. 2278–2324, Mar. 1998
1998
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne,” J. Mach. Learn. Res. , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” [Online]. Available: https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf
2009
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” in Deep Learning Workshop of Neural Inf. Process. Syst. (NeurIPS) , 2011
2011
Earlier work this paper cites.
Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in Proc. Int. Conf. Mach. Learn. (ICML) , vol. 37, Lille, France, Jul. 2015, pp. 1180–1189
2015
Earlier work this paper cites.
Y. Wen, K. Zhang, Z. Li, and Y. Qiao, “A discriminative feature learning approach for deep face recognition,” in Proc. Eur. Conf. Comp. Vision (ECCV) , vol. 9911, Amsterdam, Netherlands, Oct. 2016, pp. 499–515
2016
Earlier work this paper cites.
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz, “Pruning convolutional neural networks for resource efficient inference,” in Proc. Int. Conf. Learn. Repr. (ICLR) , Toulon, France, Apr. 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE/CVF Conf. Comput. Vision Pattern Recognit. (CVPR) , Las Vegas, NV, USA, Jun. 2016, pp. 770–778
2016
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 Proc. Int. Conf. Artif. Intell. Statist. (AISTATS) , Ft. Lauderdale, FL, USA, Apr. 2017, pp. 1273–1282
2017
Earlier work this paper cites.
J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” in Proc. Conf. Adv. Neural Inf. Process. Syst. (NeurIPS) , vol. 30, Long Beach, CA, USA, Dec. 2017
2017
Earlier work this paper cites.
Y. Movshovitz-Attias, A. Toshev, T. K. Leung, S. Ioffe, and S. Singh, “No fuss distance metric learning using proxies,” in Proc. IEEE/CVF Int. Conf. Comput. Vision (ICCV) , Venice, Italy, Oct. 2017, pp. 360–368
2017
Earlier work this paper cites.
G. Cohen, S. Afshar, J. Tapson, and A. Van Schaik, “Emnist: Extending mnist to handwritten letters,” in Proc. Int. Jt. Conf. Neural Networks (IJCNN) , Anchorage, AK, USA, May 2017, pp. 2921–2926
2017
Earlier work this paper cites.
M. Yurochkin, M. Agarwal, S. Ghosh, K. Greenewald, N. Hoang, and Y. Khazaeni, “Bayesian nonparametric federated learning of neural networks,” in Proc. Int. Conf. Mach. Learn. (ICML) , vol. 97, Long Beach, California, USA, Jun. 2019, pp. 7252–7261
2019
Earlier work this paper cites.
H. Yu, R. Jin, and S. Yang, “On the linear speedup analysis of communication efficient momentum sgd for distributed non-convex optimization,” in Proc. Int. Conf. Mach. Learn. (ICML) , vol. 97, Long Beach, California, USA, Jun. 2019, pp. 7184–7193
2019
Earlier work this paper cites.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” in Proc. Mach. Learn. Syst. (MLSys) , Austin, TX, USA, Mar. 2020
2020
Earlier work this paper cites.
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “Scaffold: Stochastic controlled averaging for federated learning,” in Proc. Int. Conf. Mach. Learn. (ICML) , vol. 119, Virtual Event, 2020, pp. 5132–5143
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,” in Proc. Conf. Adv. Neural Inf. Process. Syst. (NeurIPS) , Virtual Event, Dec. 2020, pp. 7611–7623
2020
Cited alongside, same era.
M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, “A joint learning and communications framework for federated learning over wireless networks,” IEEE Trans. Wirel. Commun. , vol. 20, no. 1, pp. 269–283, Oct. 2020
2020
Cited alongside, same era.
H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, and Y. Khazaeni, “Federated learning with matched averaging,” in Proc. Int. Conf. Learn. Repr. (ICLR) , Addis Ababa, Ethiopia, Apr. 2020
2020
A. Z. Tan, H. Yu, L. Cui, and Q. Yang, “Towards personalized federated learning,” IEEE Trans. Neural Networks Learn. Syst. , pp. 1–17, Mar. 2022
2022
Closest in time.
J. Zhang, Z. Li, B. Li, J. Xu, S. Wu, S. Ding, and C. Wu, “Federated learning with label distribution skew via logits calibration,” in Proc. Int. Conf. Mach. Learn. (ICML) . PMLR, Jul. 2022, pp. 26 311–26 329
2022
Closest in time.
Z. Li, J. Shao, Y. Mao, J. H. Wang, and J. Zhang, “Federated learning with gan-based data synthesis for non-iid clients,” in FL Workshop in Proc. Int. Joint Conf Artif. Intell (IJCAI) , vol. 13448, Vienna, Austria, Jul. 2022, pp. 17–32
2022
Closest in time.
Z. Tang, Y. Zhang, S. Shi, X. He, B. Han, and X. Chu, “Virtual homogeneity learning: Defending against data heterogeneity in federated learning,” in Proc. Int. Conf. Mach. Learn. (ICML) , vol. 162, Baltimore, Maryland, USA, Jul. 2022, pp. 21 111–21 132
2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
K. Wei, J. Li, M. Ding, C. Ma, H. Su, B. Zhang, and H. V. Poor, “User-level privacy-preserving federated learning: Analysis and performance optimization,” IEEE Trans. Mobile Comput. , vol. 21, no. 9, pp. 3388–3401, Sep. 2021
2021
Cited alongside, same era.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings et al. , “Advances and open problems in federated learning,” Found. Trends Mach. Learn. , vol. 14, no. 1–2, pp. 1–210, 2021
2021
Cited alongside, same era.
W. Zhang, D. Yang, W. Wu, H. Peng, N. Zhang, H. Zhang, and X. Shen, “Optimizing federated learning in distributed industrial iot: A multi-agent approach,” IEEE J. Sel. Areas Commun. , vol. 39, no. 12, pp. 3688–3703, Oct. 2021
2021
Cited alongside, same era.
Q. Li, B. He, and D. Song, “Model-contrastive federated learning,” in Proc. IEEE/CVF Conf. Comput. Vision Pattern Recognit. (CVPR) , Virtual Event, Jun. 2021, pp. 10 713–10 722
2021
Cited alongside, same era.
D. A. E. Acar, Y. Zhao, R. Matas, M. Mattina, P. Whatmough, and V. Saligrama, “Federated learning based on dynamic regularization,” in Proc. Int. Conf. Learn. Repr. (ICLR) , Virtual Event, May 2021
2021
Cited alongside, same era.
M. Luo, F. Chen, D. Hu, Y. Zhang, J. Liang, and J. Feng, “No fear of heterogeneity: Classifier calibration for federated learning with non-iid data,” in Proc. Conf. Adv. Neural Inf. Process. Syst. (NeurIPS) , vol. 34, Virtual Event, Dec. 2021, pp. 5972–5984
2021
Cited alongside, same era.
L. Zhang, Y. Luo, Y. Bai, B. Du, and L.-Y. Duan, “Federated learning for non-iid data via unified feature learning and optimization objective alignment,” in Proc. IEEE/CVF Int. Conf. Comput. Vision (ICCV) , Montreal, QC, Canada, Oct. 2021, pp. 4420–4428
2021
Cited alongside, same era.
S. J. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečný, S. Kumar, and H. B. McMahan, “Adaptive federated optimization,” in Proc. Int. Conf. Learn. Repr. (ICLR) , Virtual Event, May 2021
2021
Cited alongside, same era.
J. Shao, Y. Sun, S. Li, and J. Zhang, “DReS-FL: Dropout-resilient secure federated learning for non-iid clients via secret data sharing,” in Proc. Conf. Adv. Neural Inf. Process. Syst. (NeurIPS) , LA, CA, USA, May 2022
2022
Closest in time.
Y. Tan, G. Long, L. Liu, T. Zhou, Q. Lu, J. Jiang, and C. Zhang, “Fedproto: Federated prototype learning across heterogeneous clients,” in Proc. AAAI Conf. Artif. Intell. (AAAI) , vol. 1, Virtual Event, Feb. 2022, p. 3
2022
Closest in time.
Z. Wang, H. Xu, J. Liu, Y. Xu, H. Huang, and Y. Zhao, “Accelerating federated learning with cluster construction and hierarchical aggregation,” IEEE Trans. Mobile Comput. , vol. 22, no. 7, pp. 3805–3822, Jul. 2023
2023
Closest in time.
W. Sun, Y. Zhao, W. Ma, B. Guo, L. Xu, and T. Q. Duong, “Accelerating convergence of federated learning in mec with dynamic community,” IEEE Trans. Mobile Comput. , pp. 1–17, Feb. 2023
2023
Closest in time.
Y. Xu, Y. Liao, H. Xu, Z. Ma, L. Wang, and J. Liu, “Adaptive control of local updating and model compression for efficient federated learning,” IEEE Trans. Mobile Comput. , vol. 22, no. 10, pp. 5675–5689, Sep. 2023
2023
Closest in time.
X. Mu, Y. Shen, K. Cheng, X. Geng, J. Fu, T. Zhang, and Z. Zhang, “FedProc: Prototypical contrastive federated learning on non-iid data,” Future Gener. Comput. Syst. , vol. 143, pp. 93–104, Mar 2023
2023
Closest in time.
S. Itahara, T. Nishio, Y. Koda, M. Morikura, and K. Yamamoto, “Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data,” IEEE Trans. Mobile Comput. , vol. 22, no. 1, pp. 191–205, Jul. 2023
2023
Closest in time.
S. Wang, M. Chen, C. G. Brinton, C. Yin, W. Saad, and S. Cui, “Performance optimization for variable bitwidth federated learning in wireless networks,” IEEE Trans. Wirel. Commun. , Mar. 2023
2023
Closest in time.
W. Zhang, H. Liang, Y. Xu, and C. Zhang, “Reliable and privacy-preserving federated learning with anomalous users,” ZTE Communications , vol. 21, no. 1, pp. 15–24, Feb 2023
2023
Closest in time.
D. Yang, W. Zhang, Q. Ye, C. Zhang, N. Zhang, C. Huang, H. Zhang, and X. Shen, “DetFed: Dynamic resource scheduling for deterministic federated learning over time-sensitive networks,” IEEE Trans. Mobile Comput. , pp. 1–17, Aug. 2023
2023
Closest in time.
Y. Sun, J. Shao, S. Li, Y. Mao, and J. Zhang, “Stochastic coded federated learning with convergence and privacy guarantees,” in IEEE Int. Symp. Inf. Theory (ISIT) , Espoo, Finland, Aug. 2022, pp. 2028–2033
2033
Closest in time.