Research graph
References from Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering. Local targets link to admitted publications; unresolved targets remain external evidence.
Communication-efficient learning of deep networks from decentralized data
External reference
10.1109/msp.2020.2975749
10.1109/msp.2020.2975749 · External reference
Federated optimization in heterogeneous networks
2018 · External reference
Personalized federated learning with Moreau envelopes
External reference
Federated learning of a mixture of global and local models
2020 · External reference
Personalized federated learning via maximizing correlation with sparse and hierarchical extensions
2021 · External reference
10.1109/tit.2022.3192506
10.1109/tit.2022.3192506 · External reference
Multi-center federated learning
2020 · External reference
10.1609/aaai.v35i9.16960
10.1609/aaai.v35i9.16960 · External reference
FedBE: Making Bayesian model ensemble applicable to federated learning
External reference
10.3390/e23010041
10.3390/e23010041 · External reference
10.1109/mci.2022.3155327
10.1109/mci.2022.3155327 · External reference
Personalized federated learning via variational Bayesian inference
External reference
Local SGD converges fast and communicates little
External reference
10.1109/ieeeconf44664.2019.9049023
10.1109/ieeeconf44664.2019.9049023 · External reference
10.1109/tsp.2021.3115952
10.1109/tsp.2021.3115952 · External reference
One-shot federated learning
2019 · External reference
10.1109/tnnls.2019.2944481
10.1109/tnnls.2019.2944481 · External reference
FetchSGD: Communication-efficient federated learning with sketching
External reference
Hyper-sphere quantization: Communication-efficient SGD for federated learning
2019 · External reference
FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization
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Federated learning with personalization layers
2019 · External reference
Federated multi-task learning
External reference
10.1109/tnnls.2020.3015958
10.1109/tnnls.2020.3015958 · External reference
Federated meta-learning with fast convergence and efficient communication
2018 · External reference
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
External reference
10.1109/tbdata.2022.3191439
10.1109/tbdata.2022.3191439 · External reference
On the convergence of clustered federated learning
2022 · External reference
Bayesian nonparametric federated learning of neural networks
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Federated learning via posterior averaging: A new perspective and practical algorithms
External reference
Personalized federated learning with Gaussian processes
External reference
10.1007/bf02551274
10.1007/bf02551274 · External reference
10.1023/a:1007665907178
10.1023/a:1007665907178 · External reference
10.1080/01621459.2017.1285773
10.1080/01621459.2017.1285773 · External reference
Weight uncertainty in neural networks
External reference
Posterior concentration for sparse deep learning
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Efficient variational inference for sparse deep learning with theoretical guarantee
External reference
Adaptive approximation and generalization of deep neural network with intrinsic dimensionality
2020 · External reference
Convergence rates of variational inference in sparse deep learning
External reference
10.1214/19-aos1875
10.1214/19-aos1875 · External reference
10.5555/2980539.2980649
10.5555/2980539.2980649 · External reference
10.1007/s11222-007-9033-z
10.1007/s11222-007-9033-z · External reference
Mnist handwritten digit database
2010 · External reference
10.1109/5.726791
10.1109/5.726791 · External reference
Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms
2017 · External reference
Learning multiple layers of features from tiny images
2009 · External reference
10.2118/18761-ms
10.2118/18761-ms · External reference
Very deep convolutional networks for large-scale image recognition
2014 · External reference
PyTorch: An imperative style, high-performance deep learning library
External reference
Practical deep learning with Bayesian principles
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