Cross-Silo Federated Learning: Challenges and Opportunities

@article{Huang2022CrossSiloFL,
  title={Cross-Silo Federated Learning: Challenges and Opportunities},
  author={Chao Huang and Jianwei Huang and Xin Liu},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.12949}
}
—Federated learning (FL) is an emerging technology that enables the training of machine learning models from multiple clients while keeping the data distributed and private. Based on the participating clients and the model training scale, federated learning can be classified into two types: cross-device FL where clients are typically mobile devices and the client number can reach up to a scale of millions; cross-silo FL where clients are organizations or companies and the client number is… 
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