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Federated learning (FL), aimed at leveraging vast distributed datasets, confronts a crucial challenge: the heterogeneity of data across different silos.
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Langley, P · 2000
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Data classification using the dempster–shafer method
Chen, Q., Whitbrook, A., Aickelin, U., and Roadknight, C · 2014
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Weight uncertainty in neural network
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Domain generalization for object recognition with multi-task autoencoders
Ghifary, M., Kleijn, W. B., Zhang, M., and Balduzzi, D · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Federated optimization: Distributed machine learning for on-device intelligence
Konečnỳ, J., McMahan, H. B., Ramage, D., and Richtárik, P · 2016
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Differentially private federated learning: A client level perspective
Geyer, R. C., Klein, T., and Nabi, M · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Deeper, broader and artier domain generalization
Li, D., Yang, Y., Song, Y.-Z., and Hospedales, T. M · 2017
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Multiplicative normalizing flows for variational bayesian neural networks
Louizos, C. and Welling, M · 2017
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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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Neural discrete representation learning
Van Den Oord, A., Vinyals, O., et al · 2017
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Federated meta-learning with fast convergence and efficient communication
Chen, F., Luo, M., Dong, Z., Li, Z., and He, X · 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
Cited alongside, same era.
On the convergence of federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Sanjabi, M., Zaheer, M., Talwalkar, A., and Smith, V · 2018
Cited alongside, same era.
Federated learning with non-iid data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V · 2018
Cited alongside, same era.
Uncertainty-based continual learning with adaptive regularization
Discrete-valued neural communication in structured architectures enhances generalization
Liu, D., Lamb, A., Kawaguchi, K., Goyal, A., Sun, C., Mozer, M., and Bengio, Y · 2021
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Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning
Nado, Z., Band, N., Collier, M., Djolonga, J., Dusenberry, M. W., Farquhar, S., Feng, Q., Filos, A., Havasi, M., Jenatton, R., et al · 2021
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Fedfa: Federated learning with feature anchors to align features and classifiers for heterogeneous data
Zhou, T., Zhang, J., and Tsang, D. H · 2021
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Learn from others and be yourself in heterogeneous federated learning
Huang, W., Ye, M., and Du, B · 2022
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Fedsr: A simple and effective domain generalization method for federated learning
Nguyen, A. T., Torr, P., and Lim, S. N · 2022
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Ahn, H., Cha, S., Lee, D., and Moon, T · 2019
Cited alongside, same era.
Rsa: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets
Li, L., Xu, W., Chen, T., Giannakis, G. B., and Ling, Q · 2019
Cited alongside, same era.
Federated machine learning: Concept and applications
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
Cited alongside, same era.
Analyzing the role of model uncertainty for electronic health records
Dusenberry, M. W., Tran, D., Choi, E., Kemp, J., Nixon, J., Jerfel, G., Heller, K., and Dai, A. M · 2020
Cited alongside, same era.
An efficient framework for clustered federated learning
Ghosh, A., Chung, J., Yin, D., and Ramchandran, K · 2020
Cited alongside, same era.
Prevalence of neural collapse during the terminal phase of deep learning training
Papyan, V., Han, X., and Donoho, D. L · 2020
Cited alongside, same era.
The skellam mechanism for differentially private federated learning
Agarwal, N., Kairouz, P., and Liu, Z · 2021
Cited alongside, same era.
A survey of uncertainty in deep neural networks
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., et al · 2021
Cited alongside, same era.
Virtual homogeneity learning: Defending against data heterogeneity in federated learning
Tang, Z., Zhang, Y., Shi, S., He, X., Han, B., and Chu, X · 2022
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Addressing heterogeneity in federated learning via distributional transformation
Yuan, H., Hui, B., Yang, Y., Burlina, P., Gong, N. Z., and Cao, Y · 2022
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Pmfl: Partial meta-federated learning for heterogeneous tasks and its applications on real-world medical records
Zhang, T., Zhang, S., Chen, Z., Bengio, Y., and Liu, D · 2022
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Decentralized federated learning through proxy model sharing
Kalra, S., Wen, J., Cresswell, J. C., Volkovs, M., and Tizhoosh, H · 2023
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Fedcr: Personalized federated learning based on across-client common representation with conditional mutual information regularization
Zhang, H., Li, C., Dai, W., Zou, J., and Xiong, H · 2023
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Ji, C. and Luo, H · 2025
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Luo, H. and Ji, C · 2025
Closest in time.