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Title: GitHub - SMILELab-FL/FedLab: A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.

Open Graph Title: GitHub - SMILELab-FL/FedLab: A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.

X Title: GitHub - SMILELab-FL/FedLab: A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.

Description: A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research. - SMILELab-FL/FedLab

Open Graph Description: A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research. - SMILELab-FL/FedLab

X Description: A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research. - SMILELab-FL/FedLab

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zengdun@foxmail.com
zszxlsq@gmail.com

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Communication-Efficient Learning of Deep Networks from Decentralized Datahttp://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf
Federated Optimization in Heterogeneous Networkshttps://arxiv.org/abs/1812.06127
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Federated Learning Based on Dynamic Regularizationhttps://openreview.net/forum?id=B7v4QMR6Z9w
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Fair Resource Allocation in Federated Learninghttps://arxiv.org/abs/1905.10497
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Improving Federated Learning Personalization via Model Agnostic Meta Learninghttps://arxiv.org/pdf/1909.12488.pdf
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintshttps://arxiv.org/abs/1910.01991
Codehttps://github.com/felisat/clustered-federated-learning#clustered-federated-learning-model-agnostic-distributed-multi-task-optimization-under-privacy-constraints
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategieshttps://arxiv.org/abs/2010.01243
QSGD: Communication-Efficient SGD via Gradient Quantization and Encodinghttps://proceedings.neurips.cc/paper/2017/hash/6c340f25839e6acdc73414517203f5f0-Abstract.html
Federated Learning on Non-IID Data Silos: An Experimental Studyhttps://arxiv.org/abs/2102.02079
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LEAF: A Benchmark for Federated Settingshttp://arxiv.org/abs/1812.01097
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