• Corpus ID: 248427294

Compositional Federated Learning for Distributionally Robust and Meta Learning

@inproceedings{Huang2021CompositionalFL,
  title={Compositional Federated Learning for Distributionally Robust and Meta Learning},
  author={Feihu Huang and Junyi Li},
  year={2021}
}
—In the paper, we propose an effective and efficient Compositional Federated Learning (ComFedL) algorithm for solving a new compositional Federated Learning (FL) framework, which frequently appears in many data mining and machine learning problems with a hierarchical structure such as distributionally robust FL and model-agnostic meta learning (MAML). Moreover, we study the convergence analysis of our ComFedL algorithm under some mild conditions, and prove that it achieves a convergence rate of… 

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