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This work considers the category distribution heterogeneity in federated learning.
Cognitron: A self-organizing multilayered neural network
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Shen, Z.-Q. and Kong, F.-S · 2004
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Practical secure aggregation for privacy-preserving machine learning
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Optimization methods for large-scale machine learning
Bottou, L., Curtis, F. E., and Nocedal, J · 2018
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Leaf: A benchmark for federated settings
Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečnỳ, J., McMahan, H. B., Smith, V., and Talwalkar, A · 2018
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Cinic-10 is not imagenet or cifar-10
Darlow, L. N., Crowley, E. J., Antoniou, A., and Storkey, A. J · 2018
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Federated learning for mobile keyboard prediction
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2018
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The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Tschandl, P., Rosendahl, C., and Kittler, H · 2018
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Federated learning with non-iid data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V · 2018
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Codella, N., Rotemberg, V., Tschandl, P., Celebi, M. E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., et al · 2019
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Class-balanced loss based on effective number of samples
Model-contrastive federated learning
Li, Q., He, B., and Song, D · 2021
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No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
Luo, M., Chen, F., Hu, D., Zhang, Y., Liang, J., and Feng, J · 2021
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Adaptive federated optimization
Reddi, S. J., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., and McMahan, H. B · 2021
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A field guide to federated optimization
Wang, J., Charles, Z., Xu, Z., Joshi, G., McMahan, H. B., Al-Shedivat, M., Andrew, G., Avestimehr, S., Daly, K., Data, D., et al · 2021
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Deep long-tailed learning: A survey
Zhang, Y., Kang, B., Hooi, B., Yan, S., and Feng, J · 2021
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Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S · 2019
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Efficient and privacy-enhanced federated learning for industrial artificial intelligence
Hao, M., Li, H., Luo, X., Xu, G., Yang, H., and Liu, S · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H., Qi, H., and Brown, M · 2019
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Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al · 2019
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On the convergence of fedavg on non-iid data
Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
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Federated learning based on dynamic regularization
Acar, D. A. E., Zhao, Y., Matas, R., Mattina, M., Whatmough, P., and Saligrama, V · 2020
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Secure, privacy-preserving and federated machine learning in medical imaging
Kaissis, G. A., Makowski, M. R., Rückert, D., and Braren, R. F · 2020
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Zhu, Z., Hong, J., and Zhou, J · 2021
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Federated submodel optimization for hot and cold data features
Ding, Y., Niu, C., Wu, F., Tang, S., Lyu, C., Chen, G., et al · 2022
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Fedskip: Combatting statistical heterogeneity with federated skip aggregation
Fan, Z., Wang, Y., Yao, J., Lyu, L., Zhang, Y., and Tian, Q · 2022
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Feddc: Federated learning with non-iid data via local drift decoupling and correction
Gao, L., Fu, H., Li, L., Chen, Y., Xu, M., and Xu, C.-Z · 2022
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Multi-task learning as a bargaining game
Navon, A., Shamsian, A., Achituve, I., Maron, H., Kawaguchi, K., Chechik, G., and Fetaya, E · 2022
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Fedfm: Anchor-based feature matching for data heterogeneity in federated learning
Ye, R., Ni, Z., Xu, C., Wang, J., Chen, S., and Eldar, Y. C · 2022
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Fine-tuning global model via data-free knowledge distillation for non-iid federated learning
Zhang, L., Shen, L., Ding, L., Tao, D., and Duan, L.-Y · 2022
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Revisiting weighted aggregation in federated learning with neural networks
Li, Z., Lin, T., Shang, X., and Wu, C · 2023
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Federated domain generalization with generalization adjustment
Zhang, R., Xu, Q., Yao, J., Zhang, Y., Tian, Q., and Wang, Y · 2023
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