Fetching the paper…
Reading the bibliography…
In federated learning (FL), the multi-step update and data heterogeneity among clients often lead to a loss landscape with sharper minima, degenerating the performance of the resulted global model.
Simplifying neural nets by discovering flat minima
Hochreiter, S. and Schmidhuber, J · 1994
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Privacy-preserving deep learning
Shokri, R. and Shmatikov, V · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Sharp minima can generalize for deep nets
Dinh, L., Pascanu, R., Bengio, S., and Bengio, Y · 2017
Earlier work this paper cites.
On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Earlier work this paper cites.
Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S · 2017
Earlier work this paper cites.
Visualizing the loss landscape of neural nets
Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T · 2018
Earlier work this paper cites.
Group normalization
Wu, Y. and He, K · 2018
Earlier work this paper cites.
Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H., Qi, H., and Brown, M · 2019
Earlier work this paper cites.
Federated transfer reinforcement learning for autonomous driving
Liang, X., Liu, Y., Chen, T., Liu, M., and Yang, Q · 2019
Earlier work this paper cites.
Moment matching for multi-source domain adaptation
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., and Wang, B · 2019
Earlier work this paper cites.
Federated learning based on dynamic regularization
Acar, D. A. E., Zhao, Y., Matas, R., Mattina, M., Whatmough, P., and Saligrama, V · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Earlier work this paper cites.
The non-iid data quagmire of decentralized machine learning
Hsieh, K., Phanishayee, A., Mutlu, O., and Gibbons, P · 2020
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for federated learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S., and Suresh, A. T · 2020
Cited alongside, same era.
On the convergence of fedavg on non-iid data
Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z · 2020
Cited alongside, same era.
Adaptive federated optimization
Reddi, S. J., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečnỳ, J., Kumar, S., and McMahan, H. B · 2020
Cited alongside, same era.
Deep leakage from gradients
Zhu, L. and Han, S · 2020
Cited alongside, same era.
Surrogate gap minimization improves sharpness-aware training
Zhuang, J., Gong, B., Yuan, L., Cui, Y., Adam, H., Dvornek, N. C., sekhar tatikonda, s Duncan, J., and Liu, T · 2020
Cited alongside, same era.
St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning
Hu, S., Chen, L., Wu, P., Li, H., Yan, J., and Tao, D · 2022
Later among the works it cites.
Federated learning on non-iid data silos: An experimental study
Li, Q., Diao, Y., Chen, Q., and He, B · 2022
Later among the works it cites.
Make sharpness-aware minimization stronger: A sparsified perturbation approach
Mi, P., Shen, L., Ren, T., Zhou, Y., Sun, X., Ji, R., and Tao, D · 2022
Later among the works it cites.
Generalized federated learning via sharpness aware minimization
Qu, Z., Li, X., Duan, R., Liu, Y., Tang, B., and Lu, Z · 2022
Later among the works it cites.
Penalizing gradient norm for efficiently improving generalization in deep learning
Zhao, Y., Zhang, H., and Hu, X · 2022
Later among the works it cites.
Fedgamma: Federated learning with global sharpness-aware minimization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sharpness-aware minimization for efficiently improving generalization
Foret, P., Kleiner, A., Mobahi, H., and Neyshabur, B · 2021
Cited alongside, same era.
Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning
Guo, P., Wang, P., Zhou, J., Jiang, S., and Patel, V. M · 2021
Cited alongside, same era.
Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks
Kwon, J., Kim, J., Park, H., and Choi, I. K · 2021
Cited alongside, same era.
Federated split task-agnostic vision transformer for covid-19 cxr diagnosis
Park, S., Kim, G., Kim, J., Kim, B., and Ye, J. C · 2021
Cited alongside, same era.
Fedcm: Federated learning with client-level momentum
Xu, J., Wang, S., Wang, L., and Yao, A. C.-C · 2021
Cited alongside, same era.
Towards understanding sharpness-aware minimization
Andriushchenko, M. and Flammarion, N · 2022
Cited alongside, same era.
Dai, R., Yang, X., Sun, Y., Shen, L., Tian, X., Wang, M., and Zhang, Y · 2023
Later among the works it cites.
Federated learning without full labels: A survey
Jin, Y., Liu, Y., Chen, K., and Yang, Q · 2023
Later among the works it cites.
Sharpness-aware gradient matching for domain generalization
Wang, P., Zhang, Z., Lei, Z., and Zhang, L · 2023
Later among the works it cites.
Fourier-based augmentation with applications to domain generalization
Xu, Q., Zhang, R., Fan, Z., Wang, Y., Wu, Y.-Y., and Zhang, Y · 2023
Later among the works it cites.
Combating representation learning disparity with geometric harmonization
Zhou, Z., Yao, J., Hong, F., Zhang, Y., Han, B., and Wang, Y · 2023
Later among the works it cites.
Learning multi-agent communication from graph modeling perspective
Hu, S., Shen, L., Zhang, Y., and Tao, D · 2024
Closest in time.
Enhancing sharpness-aware optimization through variance suppression
Li, B. and Giannakis, G · 2024
Closest in time.
Normalization layers are all that sharpness-aware minimization needs
Mueller, M., Vlaar, T., Rolnick, D., and Hein, M · 2024
Closest in time.
Understanding how consistency works in federated learning via stage-wise relaxed initialization
Sun, Y., Shen, L., and Tao, D · 2024
Closest in time.