• Corpus ID: 238419507

Federated Learning from Small Datasets

@article{Kamp2021FederatedLF,
  title={Federated Learning from Small Datasets},
  author={Michael Kamp and Jonas Fischer and Jilles Vreeken},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.03469}
}
Federated learning allows multiple parties to collaboratively train a joint model without sharing local data. This enables applications of machine learning in settings of inherently distributed, undisclosable data such as in the medical domain. In practice, joint training is usually achieved by aggregating local models, for which local training objectives have to be in expectation similar to the joint (global) objective. Often, however, local datasets are so small that local objectives differ… 

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