Fetching the paper…
Reading the bibliography…
A central challenge in training classification models in the real-world federated system is learning with non-IID data.
Mixture models: theory, geometry and applications
Lindsay, B. G · 1995
Earlier work this paper cites.
Long short-term memory
Hochreiter, S., J. Schmidhuber · 1997
Earlier work this paper cites.
Visualizing data using t-sne
Van der Maaten, L., G. Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., W. Dong, R. Socher, et al · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., G. Hinton, et al · 2009
Earlier work this paper cites.
Generative adversarial networks
Goodfellow, I. J., J. Pouget-Abadie, M. Mirza, et al · 2014
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., J. Deng, H. Su, et al · 2015
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, B., E. Moore, D. Ramage, et al · 2017
Earlier work this paper cites.
Federated multi-task learning
Smith, V., C.-K. Chiang, M. Sanjabi, et al · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., N. Shazeer, N. Parmar, et al · 2017
Earlier work this paper cites.
Federated optimization in heterogeneous networks
Li, T., A. K. Sahu, M. Zaheer, et al · 2018
Earlier work this paper cites.
Federated learning with non-iid data
Zhao, Y., M. Li, L. Lai, et al · 2018
Earlier work this paper cites.
Jeong, E., S. Oh, H. Kim, et al · 2018
Earlier work this paper cites.
Federated meta-learning with fast convergence and efficient communication
Chen, F., M. Luo, Z. Dong, et al · 2018
Earlier work this paper cites.
No peek: A survey of private distributed deep learning
Vepakomma, P., T. Swedish, R. Raskar, et al · 2018
Earlier work this paper cites.
Cinic-10 is not imagenet or cifar-10
Darlow, L. N., E. J. Crowley, A. Antoniou, et al · 2018
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., A. Howard, M. Zhu, et al · 2018
Cited alongside, same era.
Advances and open problems in federated learning
Kairouz, P., H. B. McMahan, B. Avent, et al · 2019
Cited alongside, same era.
Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H., H. Qi, M. Brown · 2019
Cited alongside, same era.
Bayesian nonparametric federated learning of neural networks
Yurochkin, M., M. Agarwal, S. Ghosh, et al · 2019
Cited alongside, same era.
Improving federated learning personalization via model agnostic meta learning
Jiang, Y., J. Konečnỳ, K. Rush, et al · 2019
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Wang, J., Q. Liu, H. Liang, et al · 2020
Later among the works it cites.
Federated learning with matched averaging
Wang, H., M. Yurochkin, Y. Sun, et al · 2020
Later among the works it cites.
Federated learning via synthetic data
Goetz, J., A. Tewari · 2020
Later among the works it cites.
Personalized federated learning: A meta-learning approach
Fallah, A., A. Mokhtari, A. Ozdaglar · 2020
Later among the works it cites.
Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints
Sattler, F., K.-R. Müller, W. Samek · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Federated user representation learning
Bui, D., K. Malik, J. Goetz, et al · 2019
Cited alongside, same era.
Similarity of neural network representations revisited
Kornblith, S., M. Norouzi, H. Lee, et al · 2019
Cited alongside, same era.
The non-iid data quagmire of decentralized machine learning
Hsieh, K., A. Phanishayee, O. Mutlu, et al · 2019
Cited alongside, same era.
On the convergence of fedavg on non-iid data
Li, X., K. Huang, W. Yang, et al · 2019
Cited alongside, same era.
Adaptive gradient-based meta-learning methods
Khodak, M., M.-F. F. Balcan, A. S. Talwalkar · 2019
Cited alongside, same era.
Federated learning with personalization layers
Arivazhagan, M. G., V. Aggarwal, A. K. Singh, et al · 2019
Cited alongside, same era.
Robust federated learning in a heterogeneous environment
Ghosh, A., J. Hong, D. Yin, et al · 2019
Cited alongside, same era.
Federated visual classification with real-world data distribution
Hsu, T.-M. H., H. Qi, M. Brown · 2020
Later among the works it cites.
Think locally, act globally: Federated learning with local and global representations
Liang, P. P., T. Liu, L. Ziyin, et al · 2020
Later among the works it cites.
An efficient framework for clustered federated learning
Ghosh, A., J. Chung, D. Yin, et al · 2020
Later among the works it cites.
Multi-center federated learning
Xie, M., G. Long, T. Shen, et al · 2020
Later among the works it cites.
Decoupling representation and classifier for long-tailed recognition
Kang, B., S. Xie, M. Rohrbach, et al · 2020
Later among the works it cites.
Fedml: A research library and benchmark for federated machine learning
He, C., S. Li, J. So, et al · 2020
Later among the works it cites.
Federated learning on non-iid data silos: An experimental study
Li, Q., Y. Diao, Q. Chen, et al · 2021
Closest in time.
Model-contrastive federated learning
Li, Q., B. He, D. Song · 2021
Closest in time.
Federated learning based on dynamic regularization
Acar, D. A. E., Y. Zhao, R. M. Navarro, et al · 2021
Closest in time.
Towards fair federated learning with zero-shot data augmentation
Hao, W., M. El-Khamy, J. Lee, et al · 2021
Closest in time.
What makes instance discrimination good for transfer learning?
Zhao, N., Z. Wu, R. W. Lau, et al · 2021
Closest in time.