Auto-weighted Robust Federated Learning with Corrupted Data Sources

@article{Li2022AutoweightedRF,
  title={Auto-weighted Robust Federated Learning with Corrupted Data Sources},
  author={Shenghui Li and Edith Ngai and Fanghua Ye and Thiemo Voigt},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  year={2022}
}
  • Shenghui Li, Edith Ngai, T. Voigt
  • Published 14 January 2021
  • Computer Science
  • ACM Transactions on Intelligent Systems and Technology (TIST)
Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants without accessing their local data. Standard federated learning techniques that naively minimize an average loss function are vulnerable to data corruptions from outliers, systematic mislabeling, or even adversaries. In this paper, we address this challenge by proposing Auto-weighted Robust Federated Learning (ARFL), a novel approach… 

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