• Corpus ID: 240419938

FedFly: Towards Migration in Edge-based Distributed Federated Learning

@article{Ullah2021FedFlyTM,
  title={FedFly: Towards Migration in Edge-based Distributed Federated Learning},
  author={Rehmat Ullah and Di Wu and Paul Harvey and Peter Kilpatrick and Ivor T. A. Spence and Blesson Varghese},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.01516}
}
—Federated learning (FL) is a privacy-preserving distributed machine learning technique that trains models while keeping all the original data generated on devices locally. Since devices may be resource constrained, offloading can be used to improve FL performance by transferring computational work- load from devices to edge servers. However, due to mobility, devices participating in FL may leave the network during training and need to connect to a different edge server. This is challenging… 

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